Circulating biomarkers for disease

ABSTRACT

Biomarkers can be assessed for diagnostic, therapy-related or prognostic methods to identify phenotypes, such as a condition or disease, or the stage or progression of a disease. Circulating biomarkers from a bodily fluid can be used in profiling of physiological states or determining phenotypes. These include nucleic acids, protein, and circulating structures such as vesicles. Biomarkers can be used for theranostic purposes to select candidate treatment regimens for diseases, conditions, disease stages, and stages of a condition, and can also be used to determine treatment efficacy. The biomarkers can be circulating biomarkers, including vesicles and microRNA.

CROSS-REFERENCE

This application claims the benefit of U.S. Provisional PatentApplication Nos. 61/321,392, filed Apr. 6, 2010; 61/332,174, filed May6, 2010; 61/348,214, filed May 25, 2010, 61/348,685, filed May 26, 2010;61/354,125, filed Jun. 11, 2010; 61/355,387, filed Jun. 16, 2010;61/362,674, filed Jul. 8, 2010; 61/413,377, filed Nov. 12, 2010;61/322,690, filed Apr. 9, 2010; 61/334,547, filed May 13, 2010;61/370,088, filed Aug. 2, 2010; 61/391,504, filed Oct. 8, 2010;61/393,823, filed Oct. 15, 2010; 61/416,560, filed Nov. 23, 2010;61/321,407, filed Apr. 6, 2010; 61/356,974, filed Jun. 21, 2010;61/379,670, filed Sep. 2, 2010; 61/381,305, filed Sep. 9, 2010;61/383,305, filed Sep. 15, 2010; and 61/411,890, filed Nov. 9, 2010.

BACKGROUND

Biomarkers for conditions and diseases such as cancer include biologicalmolecules such as proteins, peptides, lipids, RNAs, DNA and variationsand modifications thereof.

The identification of specific biomarkers, such as DNA, RNA andproteins, can provide biosignatures that are used for the diagnosis,prognosis, or theranosis of conditions or diseases. Biomarkers can bedetected in bodily fluids, including circulating DNA, RNA, proteins, andvesicles. Circulating biomarkers include proteins such as PSA and CA125,and nucleic acids such as SEPT9 DNA and PCA3 messenger RNA (mRNA).Circulating biomarkers also include circulating vesicles. Vesicles aremembrane encapsulated structures that are shed from cells and have beenfound in a number of bodily fluids, including blood, plasma, serum,breast milk, ascites, bronchoalveolar lavage fluid and urine. Vesiclescan take part in the communication between cells as transport vehiclesfor proteins, RNAs, DNAs, viruses, and prions. MicroRNAs are short RNAsthat regulate the transcription and degradation of messenger RNAs.MicroRNAs have been found in bodily fluids and have been observed as acomponent within vesicles shed from tumor cells. The analysis ofcirculating biomarkers associated with diseases, including vesiclesand/or microRNA, can aid in detection of disease or severity thereof,determining predisposition to a disease, as well as making treatmentdecisions.

Vesicles present in a biological sample provide a source of biomarkers,e.g., the markers are present within a vesicle (vesicle payload), or arepresent on the surface of a vesicle. Characteristics of vesicles (e.g.,size, surface antigens, determination of cell-of-origin, payload) canalso provide a diagnostic, prognostic or theranostic readout. Thereremains a need to identify biomarkers that can be used to detect andtreat disease. microRNA and other biomarkers associated with vesicles aswell as the characteristics of a vesicle can provide a diagnosis,prognosis, or theranosis.

The present invention provides methods and systems for characterizing aphenotype by detecting biomarkers that are indicative of disease ordisease progress. The biomarkers can be circulating biomarkers includingvesicles and microRNA.

SUMMARY

Disclosed herein are methods and compositions for characterizing aphenotype by analyzing a vesicle, such as a vesicle present in abiological sample derived from a subject's cell. Characterizing aphenotype for a subject or individual may include, but is not limitedto, the diagnosis of a disease or condition, the prognosis of a diseaseor condition, the determination of a disease stage or a condition stage,a drug efficacy, a physiological condition, organ distress or organrejection, disease or condition progression, therapy-related associationto a disease or condition, or a specific physiological or biologicalstate.

In an aspect, the invention provides a method of characterizing aprostate disorder comprising, determining a presence or level of one ormore biomarker in a biological sample from a subject, identifying abiosignature comprising the presence or level of the one or morebiomarker, and comparing the biosignature to a reference, therebycharacterizing the prostate disorder. Useful biomarkers are listed inTable 5 or Tables 9-11 herein. The step of comparing the biosignature tothe reference can be determining whether any of the one or morebiomarker is altered relative to the reference, and thereby providing aprognostic, diagnostic or theranostic determination for the prostatedisorder.

In one embodiment, the prostate disorder comprises BPH and the one ormore biomarker is selected from the group consisting of BCMA, CEACAM-1,HVEM, IL-1 R4, IL-10 Rb, Trappin-2, p53, hsa-miR-329, hsa-miR-30a,hsa-miR-335, hsa-miR-152, hsa-miR-151-5p, hsa-miR-200a, hsa-miR-145,hsa-miR-29a, hsa-miR-106b, hsa-miR-595, hsa-miR-142-5p, hsa-miR-99a,hsa-miR-20b, hsa-miR-373, hsa-miR-502-5p, hsa-miR-29b, hsa-miR-142-3p,hsa-miR-663, hsa-miR-423-5p, hsa-miR-15a, hsa-miR-888, hsa-miR-361-3p,hsa-miR-365, hsa-miR-10b, hsa-miR-199a-3p, hsa-miR-181a, hsa-miR-19a,hsa-miR-125b, hsa-miR-760, hsa-miR-7a, hsa-miR-671-5p, hsa-miR-7c,hsa-miR-1979, hsa-miR-103 and a combination thereof.

In another embodiment, the prostate disorder comprises prostate cancerand the one or more biomarker is selected from the group consisting ofCD9, PSMA, PCSA, CD63, CD81, B7H3, IL6, OPG-13, IL6R, PA2G4, EZH2,RUNX2, SERPINB3, EpCam, hsa-let-7b, hsa-miR-107, hsa-miR-1205,hsa-miR-1270, hsa-miR-130b, hsa-miR-141, hsa-miR-143, hsa-miR-148b*,hsa-miR-150, hsa-miR-154*, hsa-miR-181a*, hsa-miR-181a-2*, hsa-miR-18a*,hsa-miR-19b-1*, hsa-miR-204, hsa-miR-2110, hsa-miR-215, hsa-miR-217,hsa-miR-219-2-3p, hsa-miR-23b*, hsa-miR-299-5p, hsa-miR-301a,hsa-miR-301a, hsa-miR-326, hsa-miR-331-3p, hsa-miR-365*, hsa-miR-373*,hsa-miR-424, hsa-miR-424*, hsa-miR-432, hsa-miR-450a, hsa-miR-451,hsa-miR-484, hsa-miR-497, hsa-miR-517*, hsa-miR-517a, hsa-miR-518f,hsa-miR-574-3p, hsa-miR-595, hsa-miR-617, hsa-miR-625*, hsa-miR-628-5p,hsa-miR-629, hsa-miR-634, hsa-miR-769-5p, hsa-miR-93, hsa-miR-96, and acombination thereof. The prostate disorder can be prostate cancer andthe one or more biomarker can be selected from the group consisting ofA33, a33 n15, AFP, ALA, ALIX, ALP, AnnexinV, APC, ASCA, ASPH (246-260),ASPH (666-680), ASPH (A-10), ASPH (D01P), ASPH (D03), ASPH (G-20),ASPH(H-300), AURKA, AURKB, B7H3, B7H4, BCA-225, BCNP1, BDNF, BRCA, CA125(MUC16), CA-19-9, C-Bir, CD1.1, CD10, CD174 (Lewis y), CD24, CD44, CD46,CD59 (MEM-43), CD63, CD66e CEA, CD73, CD81, CD9, CDA, CDAC1 1a2, CEA,C-Erb2, C-erbB2, CRMP-2, CRP, CXCL12, CYFRA21-1, DLL4, DR3, EGFR, Epcam,EphA2, EphA2 (H-77), ER, ErbB4, EZH2, FASL, FRT, FRT c.f23, GDF15, GPCR,GPR30, Gro-alpha, HAP, HBD 1, HBD2, HER 3 (ErbB3), HSP, HSP70, hVEGFR2,iC3b, IL6 Unc, IL-1B, IL6 Unc, IL6R, IL8, IL-8, INSIG-2, KLK2, L1CAM,LAMN, LDH, MACC-1, MAPK4, MART-1, MCP-1, M-CSF, MFG-E8, MIC1, MIF, MISRII, MMG, MMP26, MMP7, MMP9, MS4A1, MUC1, MUC1 seq1, MUC1 seq11A, MUC17,MUC2, Ncam, NGAL, NPGP/NPFF2, OPG, OPN, p53, p53, PA2G4, PBP, PCSA,PDGFRB, PGP9.5, PIM1, PR (B), PRL, PSA, PSMA, PSME3, PTEN, R5-CD9 Tube1, Reg IV, RUNX2, SCRN1, seprase, SERPINB3, SPARC, SPB, SPDEF, SRVN,STAT 3, STEAP1, TF (FL-295), TFF3, TGM2, TIMP-1, TIMP1, TIMP2, TMEM211,TMPRSS2, TNF-alpha, Trail-R2, Trail-R4, TrKB, TROP2, Tsg 101, TWEAK,UNC93A, VEGF A, YPSMA-1, and a combination thereof.

The characterizing may comprise detecting a prostate cancer biosignaturein the sample. Characterizing the prostate cancer can compriseidentifying the prostate cancer as metastatic or aggressive. In suchcases, the one or more biomarker can be selected from the groupconsisting of hsa-miR-100, hsa-miR-1236, hsa-miR-1296, hsa-miR-141,hsa-miR-146b-5p, hsa-miR-17*, hsa-miR-181a, hsa-miR-200b, hsa-miR-20a*,hsa-miR-23a*, hsa-miR-331-3p, hsa-miR-375, hsa-miR-452, hsa-miR-572,hsa-miR-574-3p, hsa-miR-577, hsa-miR-582-3p, hsa-miR-937, miR-10a,miR-134, miR-141, miR-200b, miR-30a, miR-32, miR-375, miR-495, miR-564,miR-570, miR-574-3p, miR-885-3p, and a combination thereof.

The characterizing can also comprise determining whether the subject isresponding to a therapeutic treatment, or whether the subject is likelyto respond or not respond to a therapeutic treatment. In an embodiment,the therapeutic treatment is selected from Tables 8-10 herein. Forexample, the therapeutic treatment comprises radical prostatectomyand/or radiation therapy. The therapeutic treatment can be selected froma standard of care for prostate cancer, including without limitation oneore more of watchful waiting, surgical pelvic lymphadenectomy, radicalprostatectomy, transurethral resection of the prostate (TURP),orchiectomy, radiation therapy, external-beam radiation therapy (EBRT),iodine I 125, palladium, iridium, hormone therapy, luteinizinghormone-releasing hormone agonists, leuprolide, goserelin, buserelin,antiandrogens, flutamide, bicalutamide, megestrol acetate, nilutamide,ketoconazole, aminoglutethimide, gonadotropin-releasing hormone (GnRH),estrogen, cryotherapy, chemotherapy, biologic therapy, ultrasound, andproton beam radiation. The biosignature for monitoring the response totreatment can include one or more of hsa-miR-1974, hsa-miR-27b,hsa-miR-103, hsa-miR-146a, hsa-miR-22, hsa-miR-382, hsa-miR-23a,hsa-miR-376c, hsa-miR-335, hsa-miR-142-5p, hsa-miR-221, hsa-miR-142-3p,hsa-miR-151-3p, hsa-miR-21, and hsa-miR-16.

In some embodiments, of the invention, the one or more biomarkercomprises one or more of 5T4, ACTG1, ADAM10, ADAM15, ALDOA, ANXA2,ANXA6, APOA1, ATP1A1, BASP1, C1orf58, C20orf114, C8B, CAPZA1, CAV1,CD151, CD2AP, CD59, CD9, CD9, CFL1, CFP, CHMP4B, CLTC, COTL1, CTNND1,CTSB, CTSZ, CYCS, DPP4, EEF1A1, EHD1, ENO1, F11R, F2, F5, FAM125A,FNBP1L, FOLH1, GAPDH, GLB1, GPX3, HIST1H1C, HIST1H2AB, HSP90AB1, HSPA1B,HSPA8, IGSF8, ITGB1, ITIH3, JUP, LDHA, LDHB, LUM, LYZ, MFGE8, MGAM,MMP9, MYH2, MYL6B, NME1, NME2, PABPC1, PABPC4, PACSIN2, PCBP2, PDCD6IP,PRDX2, PSA, PSMA, PSMA1, PSMA2, PSMA4, PSMA6, PSMA7, PSMB1, PSMB2,PSMB3, PSMB4, PSMB5, PSMB6, PSMB8, PTGFRN, RPS27A, SDCBP, SERINC5,SH3GL1, SLC3A2, SMPDL3B, SNX9, TACSTD1, TCN2, THBS1, TPI1, TSG101, TUBB,VDAC2, VPS37B, YWHAG, YWHAQ, and YWHAZ. The one or more biomarker canalso include one or more of CD9, CD63, CD81, PSMA, PCSA, B7H3 and EpCam.Each of these biomarkers can be assessed in association with a vesiclemembrane.

In another aspect, the invention provides a method of characterizingbenign prostate hyperplasia (BPH) comprising, determining a presence orlevel of one or more biomarker listed in Table 5 herein in a biologicalsample from a subject, identifying a biosignature comprising thepresence or level of the one or more biomarker, and comparing thebiosignature to a reference. The invention also provides a method ofcharacterizing a prostate cancer comprising, determining a presence orlevel of one or more biomarker listed in Table 5 herein in a biologicalsample from a subject, identifying a biosignature comprising thepresence or level of the one or more biomarker, and comparing thebiosignature to a reference. Still further, the invention providesmethod of characterizing an aggressiveness of a prostate cancercomprising, determining a presence or level of one or more biomarkerlisted in Table 5 herein in a biological sample from a subject,identifying a biosignature comprising the presence or level of the oneor more, and comparing the biosignature to a reference.

In embodiments of the subject methods, the biological sample comprises abodily fluid. The bodily fluid can comprise peripheral blood, sera,plasma, ascites, urine, cerebrospinal fluid (CSF), sputum, saliva, bonemarrow, synovial fluid, aqueous humor, amniotic fluid, cerumen, breastmilk, broncheoalveolar lavage fluid, semen, prostatic fluid, cowper'sfluid or pre-ejaculatory fluid, female ejaculate, sweat, fecal matter,hair, tears, cyst fluid, pleural and peritoneal fluid, pericardialfluid, lymph, chyme, chyle, bile, interstitial fluid, menses, pus,sebum, vomit, vaginal secretions, mucosal secretion, stool water,pancreatic juice, lavage fluids from sinus cavities, bronchopulmonaryaspirates, blastocyl cavity fluid, or umbilical cord blood. Thebiological sample can be, e.g., urine, blood or blood derivatives. Bloodderivatives include without limitation plasma and serum.

In some embodiments, the biological sample comprises one or moremicrovesicle. The one or more biomarker included in the biosignature canbe associated with the one or more microvesicle. When the biosignaturecomprises multiple markers, some markers can be associated with vesicleswhereas other markers are not. Alternately, all markers in thebiosignature can be associated with the one or more microvesicle.

The one or more microvesicle can have a diameter between 20 nm and 800nm, e.g., between 20 nm and 200 nm, between 20 nm and 100 nm, or between100 nm and 500 nm.

The one or more microvesicle can be subjected to size exclusionchromatography, density gradient centrifugation, differentialcentrifugation, nanomembrane ultrafiltration, immunoabsorbent capture,affinity purification, affinity capture, immunoassay, microfluidicseparation, flow cytometry or combinations thereof. Such methods can beuseful for isolating the one or more microvesicle, in whole or in part,from other constituents in the sample.

In some embodiments, the one or more microvesicle is contacted with oneor more binding agent. The one or more binding agent may comprise anucleic acid, DNA molecule, RNA molecule, antibody, antibody fragment,aptamer, peptoid, zDNA, peptide nucleic acid (PNA), locked nucleic acid(LNA), lectin, peptide, dendrimer, membrane protein labeling agent,chemical compound, or a combination thereof. The one or more bindingagent can be used to capture and/or detect the one or more microvesicle.For example, the one or more binding agent can bind to one or moresurface antigen on the one or more microvesicle. The one or more bindingagent can be used to capture the one or more microvesicle, e.g., whereinthe binding agent is tethered to a substrate. The one or more bindingagent can also be used to detect the one or more microvesicle, e.g.,wherein the binding agent is labeled or can also bind to a labeledmoiety.

The one or more surface antigen recognized by the binding agent can beone or more protein, e.g. a surface protein on the microvesicle. The oneor more protein may comprise one or more of CD9, CD63, CD81, PSMA, PCSA,B7H3 and EpCam. The one or more protein may comprise one or more of atetraspanin, CD9, CD63, CD81, CD63, CD9, CD81, CD82, CD37, CD53, Rab-5b,Annexin V, MFG-E8, or a protein in Table 3.

In addition to surface antigens, the one or more biomarker may comprisepayload within the one or more microvesicle. For example, a surfaceantigen can be used to capture the one or more microvesicle, then thepayload within one or more captured microvesicle is assessed.

The microvesicle payload may comprise one or more nucleic acid, peptide,protein, lipid, antigen, carbohydrate, and/or proteoglycan. The nucleicacid may comprise one or more DNA, mRNA, microRNA, snoRNA, snRNA, rRNA,tRNA, siRNA, hnRNA, or shRNA. In an embodiment, the nucleic acidcomprises one or more microRNA selected from the group consisting ofhsa-miR-200b, hsa-miR-375, hsa-miR-141, hsa-miR-331-3p, hsa-miR-181a,hsa-miR-574-3p and a combination thereof. The nucleic acid may alsocomprise one or more microRNA selected from Tables 27-41 herein.

In another aspect, the invention provides a method of characterizing acolorectal cancer comprising, determining the presence or levels of oneor more biomarker in a biological sample from a subject, identifying abiosignature comprising the presence or levels of one or more biomarkerin the biological sample, and comparing the biosignature to a reference,thereby characterizing the colorectal cancer. Useful biomarkers arelisted in Table 6 and Tables 9-11 herein. The step of comparing thebiosignature to the reference may comprise determining whether any ofthe one or more biomarker is altered relative to the reference, andthereby providing a prognostic, diagnostic or theranostic determinationfor the colorectal cancer.

In an embodiment, the one or more biomarker is selected from the groupconsisting of miR-92, miR-21, miR-9, miR-491, hsa-miR-376c, hsa-miR-215,hsa-miR-652, hsa-miR-582-5p, hsa-miR-324-5p, hsa-miR-1296,hsa-miR-28-5p, hsa-miR-190, hsa-miR-590-5p, hsa-miR-202, hsa-miR-195,and a combination thereof. In another embodiment, the one or morebiomarker comprises one or more of Muc1, GPCR 110, TMEM211 and CD24. Instill another embodiment, the one or more biomarker comprises one ormore of A33, AFP, ALIX, ALX4, ANCA, APC, ASCA, AURKA, AURKB, B7H3,BANK1, BCNP1, BDNF, CA-19-9, CCSA-2, CCSA-3&4, CD10, CD24, CD44, CD63,CD66 CEA, CD66e CEA, CD81, CD9, CDA, C-Erb2, CRMP-2, CRP, CRTN, CXCL12,CYFRA21-1, DcR3, DLL4, DR3, EGFR, Epcam, EphA2, FASL, FRT, GAL3, GDF15,GPCR (GPR110), GPR30, GRO-1, HBD 1, HBD2, HNP1-3, IL-1B, IL8, IMP3,L1CAM, LAMN, MACC-1, MGC20553, MCP-1, M-CSF, MIC1, MIF, MMP7, MMP9,MS4A1, MUC1, MUC17, MUC2, Ncam, NGAL, NNMT, OPN, p53, PCSA, PDGFRB, PRL,PSMA, PSME3, Reg IV, SCRN1, Sept-9, SPARC, SPON2, SPR, SRVN, TFF3, TGM2,TIMP-1, TMEM211, TNF-alpha, TPA, TPS, Trail-R2, Trail-R4, TrKB, TROP2,Tsg 101, TWEAK, UNC93A, and VEGFA. The one or more biomarker maycomprise one or more of CD9, EGFR, NGAL, CD81, STEAP, CD24, A33, CD66E,EPHA2, Ferritin, GPR30, GPR110, MMP9, OPN, p53, TMEM211, TROP2, TGM2,TIMP, EGFR, DR3, UNC93A, MUC17, EpCAM, MUC1, MUC2, TSG101, CD63, B7H3,CD24, and TETS.

The characterizing may comprise detecting a colorectal cancerbiosignature in the sample. The characterizing may comprise identifyingthe colorectal cancer as metastatic or aggressive.

The characterizing may also comprise determining whether the subject isresponding to a therapeutic treatment, or whether the subject is likelyto respond or not respond to a therapeutic treatment. The therapeutictreatment can be selected from Tables 8-10 herein. The therapeutictreatment can be a standard of care for the colorectal cancer, includingwithout limitation one or more of primary surgical therapy, localexcision, resection and anastomosis of primary lesion and removal ofsurrounding lymph nodes, adjuvant therapy, fluorouracil (5-FU),capecitabine, leucovorin, oxaliplatin, erlotinib, irinotecan, aspirin,mitomycin C, suntinib, cetuximab, bevacizumab, pegfilgrastim,panitumumab, ramucirumab, celecoxib, combination therapy, FOLFOX4regimen, FOLFOX6 regimen, FOLFIRI regimen, FUFOX regimen, FUOX regimen,IFL regimen, XELOX regimen, 5-FU and levamisole regimens, German AIOregimen, CAPDX regimen, Douillard regimen, and radiation therapy.

In an embodiment, the one or more biomarker comprises comprise one ormore of A26C1A, A26C1B, A2M, ACAA2, ACE, ACOT7, ACP1, ACTA1, ACTA2,ACTB, ACTBL2, ACTBL3, ACTC1, ACTG1, ACTG2, ACTN1, ACTN2, ACTN4, ACTR3,ADAM10, ADSL, AGR2, AGR3, AGRN, AHCY, AHNAK, AKR1B10, ALB, ALDH16A1,ALDH1A1, ALDOA, ANXA1, ANXA11, ANXA2, ANXA2P2, ANXA4, ANXA5, ANXA6,AP2A1, AP2A2, APOA1, ARF1, ARF3, ARF4, ARF5, ARF6, ARHGDIA, ARPC3,ARPC5L, ARRDC1, ARVCF, ASCC3L1, ASNS, ATP1A1, ATP1A2, ATP1A3, ATP1B1,ATP4A, ATP5A1, ATP5B, ATP5I, ATP5L, ATP50, ATP6AP2, B2M, BAIAP2,BAIAP2L1, BRI3BP, BSG, BUB3, C1orf58, C5orf32, CAD, CALM1, CALM2, CALM3,CAND1, CANX, CAPZA1, CBR1, CBR3, CCT2, CCT3, CCT4, CCT5, CCT6A, CCT7,CCT8, CD44, CD46, CD55, CD59, CD63, CD81, CD82, CD9, CDC42, CDH1, CDH17,CEACAM5, CFL1, CFL2, CHMP1A, CHMP2A, CHMP4B, CKB, CLDN3, CLDN4, CLDN7,CLIC1, CLIC4, CLSTN1, CLTC, CLTCL1, CLU, COL12A1, COPB1, COPB2, CORO1C,COX411, COX5B, CRYZ, CSPG4, CSRP1, CST3, CTNNA1, CTNNB1, CTNND1, CTTN,CYFIP1, DCD, DERA, DIP2A, DIP2B, DIP2C, DMBT1, DPEP1, DPP4, DYNC1H1,EDIL3, EEF1A1, EEF1A2, EEF1AL3, EEF1G, EEF2, EFNB1, EGFR, EHD1, EHD4,EIF3EIP, EIF3I, EIF4A1, EIF4A2, ENO1, ENO2, ENO3, EPHA2, EPHA5, EPHB1,EPHB2, EPHB3, EPHB4, EPPK1, ESD, EZR, F11R, F5, F7, FAM125A, FAM125B,FAM129B, FASLG, FASN, FAT, FCGBP, FER1L3, FKBP1A, FLNA, FLNB, FLOT1,FLOT2, G6PD, GAPDH, GARS, GCN1L1, GDI2, GK, GMDS, GNA13, GNAI2, GNAI3,GNAS, GNB1, GNB2, GNB2L1, GNB3, GNB4, GNG12, GOLGA7, GPA33, GPI, GPRC5A,GSN, GSTP1, H2AFJ, HADHA, hCG_(—)1757335, HEPH, HIST1H2AB, HIST1H2AE,HIST1H2AJ, HIST1H2AK, HIST1H4A, HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E,HIST1H4F, HIST1H4H, HIST1H4I, HIST1H4J, HIST1H4K, HIST1H4L, HIST2H2AC,HIST2H4A, HIST2H4B, HIST3H2A, HIST4H4, HLA-A, HLA-A29.1, HLA-B, HLA-C,HLA-E, HLA-H, HNRNPA2B1, HNRNPH2, HPCAL1, HRAS, HSD17B4, HSP90AA1,HSP90AA2, HSP90AA4P, HSP90AB1, HSP90AB2P, HSP90AB3P, HSP90B1, HSPA1A,HSPA1B, HSPA1L, HSPA2, HSPA4, HSPA5, HSPA6, HSPA7, HSPA8, HSPA9, HSPD1,HSPE1, HSPG2, HYOU1, IDH1, IFITM1, IFITM2, IFITM3, IGH@, IGHG1, IGHG2,IGHG3, IGHG4, IGHM, IGHV4-31, IGK@, IGKC, IGKV1-5, IGKV2-24, IGKV3-20,IGSF3, IGSF8, IQGAP1, IQGAP2, ITGA2, ITGA3, ITGA6, ITGAV, ITGB1, ITGB4,JUP, KIAA0174, KIAA1199, KPNB1, KRAS, KRT1, KRT10, KRT13, KRT14, KRT15,KRT16, KRT17, KRT18, KRT19, KRT2, KRT20, KRT24, KRT25, KRT27, KRT28,KRT3, KRT4, KRT5, KRT6A, KRT6B, KRT6C, KRT7, KRT75, KRT76, KRT77, KRT79,KRT8, KRT9, LAMAS, LAMP1, LDHA, LDHB, LFNG, LGALS3, LGALS3BP, LGALS4,LIMA1, LIN7A, LIN7C, LOC100128936, LOC100130553, LOC100133382,LOC100133739, LOC284889, LOC388524, LOC388720, LOC442497, LOC653269,LRP4, LRPPRC, LRSAM1, LSR, LYZ, MAN1A1, MAP4K4, MARCKS, MARCKSL1,METRNL, MFGE8, MICA, MIF, MINK1, MITD1, MMP1, MOBKL1A, MSN, MTCH2,MUC13, MYADM, MYH10, MYH11, MYH14, MYH9, MYL6, MYL6B, MYO1C, MYO1D,NARS, NCALD, NCSTN, NEDD4, NEDD4L, NME1, NME2, NOTCH1, NQO1, NRAS, P4HB,PCBP1, PCNA, PCSK9, PDCD6, PDCD6IP, PDIA3, PDXK, PEBP1, PFN1, PGK1, PHB,PHB2, PKM2, PLEC1, PLEKHB2, PLSCR3, PLXNA1, PLXNB2, PPIA, PPIB, PPP2R1A,PRDX1, PRDX2, PRDX3, PRDX5, PRDX6, PRKAR2A, PRKDC, PRSS23, PSMA2, PSMC6,PSMD11, PSMD3, PSME3, PTGFRN, PTPRF, PYGB, QPCT, QSOX1, RAB10, RAB11A,RAB11B, RAB13, RAB14, RAB15, RAB1A, RAB1B, RAB2A, RAB33B, RAB35, RAB43,RAB4B, RAB5A, RAB5B, RAB5C, RAB6A, RAB6B, RAB7A, RAB8A, RAB8B, RAC1,RAC3, RALA, RALB, RAN, RANP1, RAP1A, RAP1B, RAP2A, RAP2B, RAP2C, RDX,REG4, RHOA, RHOC, RHOG, ROCK2, RP11-631M21.2, RPL10A, RPL12, RPL6, RPL8,RPLP0, RPLP0-like, RPLP1, RPLP2, RPN1, RPS13, RPS14, RPS15A, RPS16,RPS18, RPS20, RPS21, RPS27A, RPS3, RPS4X, RPS4Y1, RPS4Y2, RPS7, RPS8,RPSA, RPSAP15, RRAS, RRAS2, RUVBL1, RUVBL2, S100A10, S100A11, S100A14,S100A16, S100A6, S100P, SDC1, SDC4, SDCBP, SDCBP2, SERINC1, SERINC5,SERPINA1, SERPINF1, SETD4, SFN, SLC12A2, SLC12A7, SLC16A1, SLC1A5,SLC25A4, SLC25A5, SLC25A6, SLC29A1, SLC2A1, SLC3A2, SLC44A1, SLC7A5,SLC9A3R1, SMPDL3B, SNAP23, SND1, SOD1, SORT1, SPTAN1, SPTBN1, SSBP1,SSR4, TACSTD1, TAGLN2, TBCA, TCEB1, TCP1, TF, TFRC, THBS1, TJP2, TKT,TMED2, TNFSF10, TNIK, TNKS1BP1, TNPO3, TOLLIP, TOMM22, TPI1, TPM1,TRAP1, TSG101, TSPAN1, TSPAN14, TSPAN15, TSPAN6, TSPAN8, TSTA3, TTYH3,TUBA1A, TUBA1B, TUBA1C, TUBA3C, TUBA3D, TUBA3E, TUBA4A, TUBA4B, TUBA8,TUBB, TUBB2A, TUBB2B, TUBB2C, TUBB3, TUBB4, TUBB4Q, TUBB6, TUFM, TXN,UBA1, UBA52, UBB, UBC, UBE2N, UBE2V2, UGDH, UQCRC2, VAMP1, VAMP3, VAMPS,VCP, VIL1, VPS25, VPS28, VPS35, VPS36, VPS37B, VPS37C, WDR1, YWHAB,YWHAE, YWHAG, YWHAH, YWHAQ, and YWHAZ. The biomarkers can be associatedwith the membrane of a vesicle.

In embodiments, the biological sample comprises a bodily fluid. Thebodily fluid can comprise peripheral blood, sera, plasma, ascites,urine, cerebrospinal fluid (CSF), sputum, saliva, bone marrow, synovialfluid, aqueous humor, amniotic fluid, cerumen, breast milk,broncheoalveolar lavage fluid, semen, prostatic fluid, cowper's fluid orpre-ejaculatory fluid, female ejaculate, sweat, fecal matter, hair,tears, cyst fluid, pleural and peritoneal fluid, pericardial fluid,lymph, chyme, chyle, bile, interstitial fluid, menses, pus, sebum,vomit, vaginal secretions, mucosal secretion, stool water, pancreaticjuice, lavage fluids from sinus cavities, bronchopulmonary aspirates,blastocyl cavity fluid, or umbilical cord blood. The biological samplecan be, e.g., stool, blood or blood derivatives. Blood derivativesinclude without limitation plasma and serum.

In some embodiments, the biological sample comprises one or moremicrovesicle. The one or more biomarker included in the biosignature canbe associated with the one or more microvesicle. When the biosignaturecomprises multiple markers, some markers can be associated with vesicleswhereas other markers are not. Alternately, all markers in thebiosignature can be associated with the one or more microvesicle.

The one or more microvesicle can have a diameter between 20 nm and 800nm, e.g., between 20 nm and 200 nm, between 20 nm and 100 nm, or between100 nm and 500 nm.

The one or more microvesicle can be subjected to size exclusionchromatography, density gradient centrifugation, differentialcentrifugation, nanomembrane ultrafiltration, immunoabsorbent capture,affinity purification, affinity capture, immunoassay, microfluidicseparation, flow cytometry or combinations thereof. Such methods can beuseful for isolating the one or more microvesicle, in whole or in part,from other constituents in the sample.

In some embodiments, the one or more microvesicle is contacted with oneor more binding agent. The one or more binding agent may comprise anucleic acid, DNA molecule, RNA molecule, antibody, antibody fragment,aptamer, peptoid, zDNA, peptide nucleic acid (PNA), locked nucleic acid(LNA), lectin, peptide, dendrimer, membrane protein labeling agent,chemical compound, or a combination thereof. The one or more bindingagent can be used to capture and/or detect the one or more microvesicle.For example, the one or more binding agent can bind to one or moresurface antigen on the one or more microvesicle. The one or more bindingagent can be used to capture the one or more microvesicle, e.g., whereinthe binding agent is tethered to a substrate. The one or more bindingagent can also be used to detect the one or more microvesicle, e.g.,wherein the binding agent is labeled or can also bind to a labeledmoiety.

The one or more surface antigen recognized by the binding agent can beone or more protein, e.g. a surface protein on the microvesicle. The oneor more protein may comprise one or more of a tetraspanin, CD9, CD63,CD81, CD63, CD9, CD81, CD82, CD37, CD53, Rab-5b, Annexin V, MFG-E8, or aprotein in Table 3 herein.

In addition to surface antigens, the one or more biomarker may comprisepayload within the one or more microvesicle. For example, a surfaceantigen can be used to capture the one or more microvesicle, then thepayload within one or more captured microvesicle is assessed.

The microvesicle payload may comprise one or more nucleic acid, peptide,protein, lipid, antigen, carbohydrate, and/or proteoglycan. The nucleicacid may comprise one or more DNA, mRNA, microRNA, snoRNA, snRNA, rRNA,tRNA, siRNA, hnRNA, or shRNA. In an embodiment, the nucleic acidcomprises one or more microRNA selected from the Table 6 herein, or acombination thereof.

The various methods of the invention can be performed in vitro.

In another aspect, the invention provides the use of a reagent to carryout any of the methods of the invention. The reagent can be used for thediagnosis, prognosis or theranosis of a disease or disorder, e.g., aprostate disorder or a colorectal disorder. In a related aspect, theinvention also provides a kit comprising a reagent to carry out any ofthe methods of the invention.

In still another aspect, the invention provides a composition comprisingan isolated vesicle. In an embodiment, the isolated vesicle comprisesone or more biomarker selected from Table 5 herein. In anotherembodiment, the isolated vesicle comprises one or more biomarkerselected from Table 6 herein. In still other embodiments, the isolatedvesicle comprises one or more biomarker selected from Tables 9-11herein.

INCORPORATION BY REFERENCE

All publications, patents and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent or patent application wasspecifically and individually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 (a)-(g) represents a table which lists exemplary cancers bylineage, group comparisons of cells/tissue, and specific disease statesand antigens specific to those cancers, group cell/tissue comparisonsand specific disease states. Furthermore, the antigen can be abiomarker. The one or more biomarkers can be present or absent,underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 2 (a)-(f) represents a table which lists exemplary cancers bylineage, group comparisons of cells/tissue, and specific disease statesand binding agents specific to those cancers, group cell/tissuecomparisons and specific disease states.

FIG. 3 (a)-(b) represents a table which lists exemplary breast cancerbiomarkers that can be derived and analyzed from a vesicle specific tobreast cancer to create a breast cancer specific vesicle biosignature.Furthermore, the one or more biomarkers can be present or absent,underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 4 (a)-(b) represents a table which lists exemplary ovarian cancerbiomarkers that can be derived from and analyzed from a vesicle specificto ovarian cancer to create an ovarian cancer specific biosignature.Furthermore, the one or more biomarkers can be present or absent,underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 5 represents a table which lists exemplary lung cancer biomarkersthat can be derived from and analyzed from a vesicle specific to lungcancer to create a lung cancer specific biosignature. Furthermore, theone or more biomarkers can be present or absent, underexpressed oroverexpressed, mutated, or modified, such as epigentically modified orpost-translationally modified.

FIG. 6 (a)-(d) represents a table which lists exemplary colon cancerbiomarkers that can be derived from and analyzed from a vesicle specificto colon cancer to create a colon cancer specific biosignature.Furthermore, the one or more biomarkers can be present or absent,underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 7 represents a table which lists exemplary biomarkers specific toan adenoma versus a hyperplastic polyp that can be derived and analyzedfrom a vesicle specific to adenomas versus hyperplastic polyps.Furthermore, the one or more biomarkers can be present or absent,underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 8 is a table which lists exemplary biomarkers specific toinflammatory bowel disease (IBD) versus normal tissue that can bederived and analyzed from a vesicle specific inflammatory bowel diseaseversus normal tissue. Furthermore, the one or more biomarkers can bepresent or absent, underexpressed or overexpressed, mutated, ormodified, such as epigentically modified or post-translationallymodified.

FIG. 9( a)-(c) represents a table which lists exemplary biomarkersspecific to an adenoma versus colorectal cancer (CRC) that can bederived and analyzed from a vesicle specific to adenomas versuscolorectal cancer. Furthermore, the one or more biomarkers can bepresent or absent, underexpressed or overexpressed, mutated, ormodified, such as epigentically modified or post-translationallymodified.

FIG. 10 represents a table which lists exemplary biomarkers specific toIBD versus CRC that can be derived and analyzed from a vesicle specificto IBD versus CRC. Furthermore, the one or more biomarkers can bepresent or absent, underexpressed or overexpressed, mutated, ormodified, such as epigentically modified or post-translationallymodified.

FIG. 11 (a)-(b) represents a table which lists exemplary biomarkersspecific to CRC Dukes B versus Dukes C-D that can be derived andanalyzed from a vesicle specific to CRC Dukes B versus Dukes C-D.Furthermore, the one or more biomarkers can be present or absent,underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 12( a)-(d) represents a table which lists exemplary biomarkersspecific to an adenoma with low grade dysplasia versus an adenoma withhigh grade dysplasia that can be derived and analyzed from a vesiclespecific to an adenoma with low grade dysplasia versus an adenoma withhigh grade dysplasia. Furthermore, the one or more biomarkers can bepresent or absent, underexpressed or overexpressed, mutated, ormodified, such as epigentically modified or post-translationallymodified.

FIG. 13( a)-(b) represents a table which lists exemplary biomarkersspecific to ulcerative colitis (UC) versus Crohn's Disease (CD) that canbe derived and analyzed from a vesicle specific to UC versus CD.Furthermore, the one or more biomarkers can be present or absent,underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 14 represents a table which lists exemplary biomarkers specific toa hyperplastic polyp versus normal tissue that can be derived andanalyzed from a vesicle specific to a hyperplastic polyp versus normaltissue. Furthermore, the one or more biomarkers can be present orabsent, underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 15 is a table which lists exemplary biomarkers specific to anadenoma with low grade dysplasia versus normal tissue that can bederived and analyzed from a vesicle specific to an adenoma with lowgrade dysplasia versus normal tissue. Furthermore, the one or morebiomarkers can be present or absent, underexpressed or overexpressed,mutated, or modified, such as epigentically modified orpost-translationally modified.

FIG. 16 is a table which lists exemplary biomarkers specific to anadenoma versus normal tissue that can be derived and analyzed from avesicle specific to an adenoma versus normal tissue. Furthermore, theone or more biomarkers can be present or absent, underexpressed oroverexpressed, mutated, or modified, such as epigentically modified orpost-translationally modified.

FIG. 17 represents a table which lists exemplary biomarkers specific toCRC versus normal tissue that can be derived and analyzed from a vesiclespecific to CRC versus normal tissue. Furthermore, the one or morebiomarkers can be present or absent, underexpressed or overexpressed,mutated, or modified, such as epigentically modified orpost-translationally modified.

FIG. 18 is a table which lists exemplary biomarkers specific to benignprostatic hyperplasia that can be derived from and analyzed from avesicle specific to benign prostatic hyperplasia. Furthermore, the oneor more biomarkers can be present or absent, underexpressed oroverexpressed, mutated, or modified, such as epigentically modified orpost-translationally modified.

FIG. 19( a)-(c) represents a table which lists exemplary prostate cancerbiomarkers that can be derived from and analyzed from a vesicle specificto prostate cancer to create a prostate cancer specific, biosignature.Furthermore, the one or more biomarkers can be present or absent,underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 20( a)-(c) represents a table which lists exemplary melanomabiomarkers that can be derived from and analyzed from a vesicle specificto melanoma to create a melanoma specific biosignature. Furthermore, theone or more biomarkers can be present or absent, underexpressed oroverexpressed, mutated, or modified, such as epigentically modified orpost-translationally modified.

FIG. 21( a)-(b) represents a table which lists exemplary pancreaticcancer biomarkers that can be derived from and analyzed from a vesiclespecific to pancreatic cancer to create a pancreatic cancer specificbiosignature. Furthermore, the one or more biomarkers can be present orabsent, underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 22 is a table which lists exemplary biomarkers specific to braincancer that can be derived from and analyzed from a vesicle specific tobrain cancer to create a brain cancer specific biosignature.Furthermore, the one or more biomarkers can be present or absent,underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 23( a)-(b) represents a table which lists exemplary psoriasisbiomarkers that can be derived from and analyzed from a vesicle specificto psoriasis to create a psoriasis specific biosignature. Furthermore,the one or more biomarkers can be present or absent, underexpressed oroverexpressed, mutated, or modified, such as epigentically modified orpost-translationally modified.

FIG. 24( a)-(c) represents a table which lists exemplary cardiovasculardisease biomarkers that can be derived from and analyzed from a vesiclespecific to cardiovascular disease to create a cardiovascular diseasespecific biosignature. Furthermore, the one or more biomarkers can bepresent or absent, underexpressed or overexpressed, mutated, ormodified, such as epigentically modified or post-translationallymodified.

FIG. 25 is a table which lists exemplary biomarkers specific tohematological malignancies that can be derived from and analyzed from avesicle specific to hematological malignancies to create a specificbiosignature for hematological malignancies. Furthermore, the one ormore biomarkers can be present or absent, underexpressed oroverexpressed, mutated, or modified, such as epigentically modified orpost-translationally modified.

FIG. 26( a)-(b) represents a table which lists exemplary biomarkersspecific to B-Cell Chronic Lymphocytic Leukemias that can be derivedfrom and analyzed from a vesicle specific to B-Cell Chronic LymphocyticLeukemias to create a specific biosignature for B-Cell ChronicLymphocytic Leukemias. Furthermore, the one or more biomarkers can bepresent or absent, underexpressed or overexpressed, mutated, ormodified, such as epigentically modified or post-translationallymodified.

FIG. 27 is a table which lists exemplary biomarkers specific to B-CellLymphoma and B-Cell Lymphoma-DLBCL that can be derived from and analyzedfrom a vesicle specific to B-Cell Lymphoma and B-Cell Lymphoma-DLBCL.Furthermore, the one or more biomarkers can be present or absent,underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 28 represents a table which lists exemplary biomarkers specific toB-Cell Lymphoma-DLBCL-germinal center-like and B-CellLymphoma-DLBCL-activated B-cell-like and B-cell lymphoma-DLBCL that canbe derived from and analyzed from a vesicle specific to B-CellLymphoma-DLBCL-germinal center-like and B-Cell Lymphoma-DLBCL-activatedB-cell-like and B-cell lymphoma-DLBCL. Furthermore, the one or morebiomarkers can be present or absent, underexpressed or overexpressed,mutated, or modified, such as epigentically modified orpost-translationally modified.

FIG. 29 represents a table which lists exemplary Burkitt's lymphomabiomarkers that can be derived from and analyzed from a vesicle specificto Burkitt's lymphoma to create a Burkitt's lymphoma specificbiosignature. Furthermore, the one or more biomarkers can be present orabsent, underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 30( a)-(b) represents a table which lists exemplary hepatocellularcarcinoma biomarkers that can be derived from and analyzed from avesicle specific to hepatocellular carcinoma to create a specificbiosignature for hepatocellular carcinoma. Furthermore, the one or morebiomarkers can be present or absent, underexpressed or overexpressed,mutated, or modified, such as epigentically modified orpost-translationally modified.

FIG. 31 is a table which lists exemplary biomarkers for cervical cancerthat can be derived from and analyzed from a vesicle specific tocervical cancer. Furthermore, the one or more biomarkers can be presentor absent, underexpressed or overexpressed, mutated, or modified, suchas epigentically modified or post-translationally modified.

FIG. 32 represents a table which lists exemplary biomarkers forendometrial cancer that can be derived from and analyzed from a vesiclespecific to endometrial cancer to create a specific biosignature forendometrial cancer. Furthermore, the one or more biomarkers can bepresent or absent, underexpressed or overexpressed, mutated, ormodified, such as epigentically modified or post-translationallymodified.

FIG. 33( a)-(b) represents a table which lists exemplary biomarkers forhead and neck cancer that can be derived from and analyzed from avesicle specific to head and neck cancer to create a specificbiosignature for head and neck cancer. Furthermore, the one or morebiomarkers can be present or absent, underexpressed or overexpressed,mutated, or modified, such as epigentically modified orpost-translationally modified.

FIG. 34 represents a table which lists exemplary biomarkers forinflammatory bowel disease (IBD) that can be derived from and analyzedfrom a vesicle specific to IBD to create a specific biosignature forIBD. Furthermore, the one or more biomarkers can be present or absent,underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 35 is a table which lists exemplary biomarkers for diabetes thatcan be derived from and analyzed from a vesicle specific to diabetes tocreate a specific biosignature for diabetes. Furthermore, the one ormore biomarkers can be present or absent, underexpressed oroverexpressed, mutated, or modified, such as epigentically modified orpost-translationally modified.

FIG. 36 is a table which lists exemplary biomarkers for Barrett'sEsophagus that can be derived from and analyzed from a vesicle specificto Barrett's Esophagus to create a specific biosignature for Barrett'sEsophagus. Furthermore, the one or more biomarkers can be present orabsent, underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 37 is a table which lists exemplary biomarkers for fibromyalgiathat can be derived from and analyzed from a vesicle specific tofibromyalgia. Furthermore, the one or more biomarkers can be present orabsent, underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 38 represents a table which lists exemplary biomarkers for strokethat can be derived from and analyzed from a vesicle specific to stroketo create a specific biosignature for stroke. Furthermore, the one ormore biomarkers can be present or absent, underexpressed oroverexpressed, mutated, or modified, such as epigentically modified orpost-translationally modified.

FIG. 39 is a table which lists exemplary biomarkers for MultipleSclerosis (MS) that can be derived from and analyzed from a vesiclespecific to MS to create a specific biosignature for MS. Furthermore,the one or more biomarkers can be present or absent, underexpressed oroverexpressed, mutated, or modified, such as epigentically modified orpost-translationally modified.

FIG. 40( a)-(b) represents a table which lists exemplary biomarkers forParkinson's Disease that can be derived from and analyzed from a vesiclespecific to Parkinson's Disease to create a specific biosignature forParkinson's Disease. Furthermore, the one or more biomarkers can bepresent or absent, underexpressed or overexpressed, mutated, ormodified, such as epigentically modified or post-translationallymodified.

FIG. 41 represents a table which lists exemplary biomarkers forRheumatic Disease that can be derived from and analyzed from a vesiclespecific to Rheumatic Disease to create a specific biosignature forRheumatic Disease. Furthermore, the one or more biomarkers can bepresent or absent, underexpressed or overexpressed, mutated, ormodified, such as epigentically modified or post-translationallymodified.

FIG. 42( a)-(b) represents a table which lists exemplary biomarkers forAlzheimer's Disease that can be derived from and analyzed from a vesiclespecific to Alzheimer's Disease to create a specific biosignature forAlzheimer's Disease. Furthermore, the one or more biomarkers can bepresent or absent, underexpressed or overexpressed, mutated, ormodified, such as epigentically modified or post-translationallymodified.

FIG. 43 is a table which lists exemplary biomarkers for Prion Diseasesthat can be derived from and analyzed from a vesicle specific to PrionDiseases to create a specific biosignature for Prion Diseases.Furthermore, the one or more biomarkers can be present or absent,underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 44 represents a table which lists exemplary biomarkers for sepsisthat can be derived from and analyzed from a vesicle specific to sepsisto create a specific biosignature for sepsis. Furthermore, the one ormore biomarkers can be present or absent, underexpressed oroverexpressed, mutated, or modified, such as epigentically modified orpost-translationally modified.

FIG. 45 is a table which lists exemplary biomarkers for chronicneuropathic pain that can be derived from and analyzed from a vesiclespecific to chronic neuropathic pain. Furthermore, the one or morebiomarkers can be present or absent, underexpressed or overexpressed,mutated, or modified, such as epigentically modified orpost-translationally modified.

FIG. 46 is a table which lists exemplary biomarkers for peripheralneuropathic pain that can be derived from and analyzed from a vesiclespecific to peripheral neuropathic pain. Furthermore, the one or morebiomarkers can be present or absent, underexpressed or overexpressed,mutated, or modified, such as epigentically modified orpost-translationally modified.

FIG. 47 represents a table which lists exemplary biomarkers forSchizophrenia that can be derived from and analyzed from a vesiclespecific to Schizophrenia to create a specific biosignature forSchizophrenia. Furthermore, the one or more biomarkers can be present orabsent, underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 48 is a table which lists exemplary biomarkers for bipolar disorderor disease that can be derived from and analyzed from a vesicle specificto bipolar disorder to create a specific biosignature for bipolardisorder. Furthermore, the one or more biomarkers can be present orabsent, underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 49 is a table which lists exemplary biomarkers for depression thatcan be derived from and analyzed from a vesicle specific to depressionto create a specific biosignature for depression. Furthermore, the oneor more biomarkers can be present or absent, underexpressed oroverexpressed, mutated, or modified, such as epigentically modified orpost-translationally modified.

FIG. 50 is a table which lists exemplary biomarkers for gastrointestinalstromal tumor (GIST) that can be derived from and analyzed from avesicle specific to GIST to create a specific biosignature for GIST.Furthermore, the one or more biomarkers can be present or absent,underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 51( a)-(b) represent sa table which lists exemplary biomarkers forrenal cell carcinoma (RCC) that can be derived from and analyzed from avesicle specific to RCC to create a specific biosignature for RCC.Furthermore, the one or more biomarkers can be present or absent,underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 52 is a table which lists exemplary biomarkers for cirrhosis thatcan be derived from and analyzed from a vesicle specific to cirrhosis tocreate a specific biosignature for cirrhosis. Furthermore, the one ormore biomarkers can be present or absent, underexpressed oroverexpressed, mutated, or modified, such as epigentically modified orpost-translationally modified.

FIG. 53 is a table which lists exemplary biomarkers for esophagealcancer that can be derived from and analyzed from a vesicle specific toesophageal cancer to create a specific biosignature for esophagealcancer. Furthermore, the one or more biomarkers can be present orabsent, underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 54 is a table which lists exemplary biomarkers for gastric cancerthat can be derived from and analyzed from a vesicle specific to gastriccancer to create a specific biosignature for gastric cancer.Furthermore, the one or more biomarkers can be present or absent,underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 55 is a table which lists exemplary biomarkers for autism that canbe derived from and analyzed from a vesicle specific to autism to createa specific biosignature for autism. Furthermore, the one or morebiomarkers can be present or absent, underexpressed or overexpressed,mutated, or modified, such as epigentically modified orpost-translationally modified.

FIG. 56 is a table which lists exemplary biomarkers for organ rejectionthat can be derived from and analyzed from a vesicle specific to organrejection to create a specific biosignature for organ rejection.Furthermore, the one or more biomarkers can be present or absent,underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 57 is a table which lists exemplary biomarkers formethicillin-resistant staphylococcus aureus that can be derived from andanalyzed from a vesicle specific to methicillin-resistant staphylococcusaureus to create a specific biosignature for methicillin-resistantstaphylococcus aureus. Furthermore, the one or more biomarkers can bepresent or absent, underexpressed or overexpressed, mutated, ormodified, such as epigentically modified or post-translationallymodified.

FIG. 58 is a table which lists exemplary biomarkers for vulnerableplaque that can be derived from and analyzed from a vesicle specific tovulnerable plaque to create a specific biosignature for vulnerableplaque. Furthermore, the one or more biomarkers can be present orabsent, underexpressed or overexpressed, mutated, or modified, such asepigentically modified or post-translationally modified.

FIG. 59( a)-(i) is a table which lists exemplary gene fusions that canbe derived from, or analyzed from a vesicle. The gene fusion can bebiomarker, and can be present or absent, underexpressed oroverexpressed, or modified, such as epigentically modified orpost-translationally modified.

FIG. 60( a)-(b) is a table of genes and their associated miRNAs, ofwhich the gene, such as the mRNA of the gene, their associated miRNAs,or any combination thereof, can be used as one or more biomarkers thatcan be analyzed from a vesicle. Furthermore, the one or more biomarkerscan be present or absent, underexpressed or overexpressed, mutated, ormodified.

FIG. 61A depicts a method of identifying a biosignature comprisingnucleic acid to characterize a phenotype. FIG. 61B depicts a method ofidentifying a biosignature of a vesicle or vesicle population tocharacterize a phenotype.

FIG. 62 illustrates results obtained from screening for proteins onvesicles, which can be used as a biomarker for the vesicles. Antibodiesto the proteins can be used as binding agents. Examples of proteinsidentified as a biomarker for a vesicle include Bcl-XL, ERCC1, Keratin15, CD81/TAPA-1, CD9, Epithelial Specific Antigen (ESA), and Mast CellChymase. The biomarker can be present or absent, underexpressed oroverexpressed, mutated, or modified in or on a vesicle and used incharacterizing a condition.

FIG. 63 illustrates methods of characterizing a phenotype by assessingvesicle biosignatures. FIG. 63A is a schematic of a planar substratecoated with a capture antibody, which captures vesicles expressing thatprotein. The capture antibody is for a vesicle protein that is specificor not specific for vesicles derived from diseased cells (“diseasevesicle”). The detection antibody binds to the captured vesicle andprovides a fluorescent signal. The detection antibody can detect anantigen that is generally associated with vesicles, or is associatedwith a cell-of-origin or a disease, e.g., a cancer. FIG. 63B is aschematic of a bead coated with a capture antibody, which capturesvesicles expressing that protein. The capture antibody is for a vesicleprotein that is specific or not specific for vesicles derived fromdiseased cells (“disease vesicle”). The detection antibody binds to thecaptured vesicle and provides a fluorescent signal. The detectionantibody can detect an antigen that is generally associated withvesicles, or is associated with a cell-of-origin or a disease, e.g., acancer. FIG. 63C is an example of a screening scheme that can beperformed by multiplexing using the beads as shown in FIG. 63B. FIG. 63Dpresents illustrative schemes for capturing and detecting vesicles tocharacterize a phenotype. FIG. 63E presents illustrative schemes forassessing vesicle payload to characterize a phenotype.

FIG. 64 is a schematic of protein expression patterns. Differentproteins are typically not distributed evenly or uniformly on a vesicleshell. Vesicle-specific proteins are typically more common, whilecancer-specific proteins are less common Capture of a vesicle can bemore easily accomplished using a more common, less cancer-specificprotein, and cancer-specific proteins used in the detection phase.

FIG. 65 illustrates a computer system that can be used in some exemplaryembodiments of the invention.

FIGS. 66A-B depict scanning electron micrographs (SEMs) of EpCamconjugated beads that have been incubated with VCaP vesicles.

FIG. 67 illustrates a method of depicting results using a bead basedmethod of detecting vesicles from a subject. FIG. 67A For an individualpatient, a graph of the bead enumeration and signal intensity using ascreening scheme as depicted in FIG. 63B, where ˜100 capture beads areused for each capture/detection combination assay per patient. For agiven patient, the output shows number of beads detected vs. intensityof signal. The number of beads captured at a given intensity is anindication of how frequently a vesicle expresses the detection proteinat that intensity. The more intense the signal for a given bead, thegreater the expression of the detection protein. FIG. 67B is anormalized graph obtained by combining normal patients into one curveand cancer patients into another, and using bio-statistical analysis todifferentiate the curves. Data from each individual is normalized toaccount for variation in the number of beads read by the detectionmachine, added together, and then normalized again to account for thedifferent number of samples in each population.

FIG. 68 illustrates prostate cancer biosignatures. FIG. 68A is ahistogram of intensity values collected from a multiplexing experimentusing a microsphere platform, where beads were functionalized with CD63antibody, incubated with vesicles purified from patient plasma, and thenlabeled with a phycoerythrin (PE) conjugated EpCam antibody. The darkershaded bars (blue) represent the population from 12 normal subjects andthe lighter shaded bars (green) are from 7 stage 3 prostate cancerpatients. FIG. 68B is a normalized graph for each of the histogramsshown in FIG. 68A, as described in FIG. 67. The distributions are of aGaussian fit to intensity values from the microsphere results of FIG.68A for both prostate patient samples and normal samples. FIG. 68C is anexample of one of the prostate biosignatures shown in FIG. 68B, the CD63versus CD63 biosignature (upper graph) where CD63 is used as thedetector and capture antibody. The lower three panels show the resultsof flow cytometry on three prostate cancer cell lines (VCaP, LNcap, and22RV1). Points above the horizontal line indicate beads that capturedvesicles with CD63 that contain B7H3. Beads to the right of the verticalline indicate beads that have captured vesicles with CD63 that havePSMA. Those beads that are above and to the right of the lines have allthree antigens. CD63 is a surface protein that is associated withvesicles, PSMA is surface protein that is associated with prostatecells, and B7H3 is a surface protein that is associated with aggressivecancers (specifically prostate, ovarian, and non-small-cell lung). Thecombination of all three antigens together identifies vesicles that arefrom cancer prostate cells. The majority of CD63 expressing prostatecancer vesicles also have prostate-specific membrane antigen, PSMA, andB7H3 (implicated in regulation of tumor cell migration and invasion andan indicator of aggressive cancer as well as clinical outcome). FIG. 68Dis a prostate cancer vesicle topography. The upper panels show theresults of capturing and labeling with CD63, CD9, and CD81 in variouscombinations. Almost all points are in the upper right quadrantindicating that these three markers are highly coupled. The lower rowdepicts the results of capturing cell line vesicles with B7H3 andlabeling with CD63 and PSMA. Both VCaP and 22RV1 show that most vesiclescaptured with B7H3 also have CD63, and that there are two populations,those with PSMA and those without. The presence of B7H3 may be anindication of how aggressive the cancer is, as LNcap does not have ahigh amount of B7H3 containing vesicles (not many spots with CD63).LnCap is an earlier stage prostate cancer analogue cell line.

FIG. 69 illustrates colon cancer biosignatures. (A) depicts histogramsof intensity values collected from various multiplexing experimentsusing a microsphere platform, where beads were functionalized with acapture antibody, incubated with vesicles purified form patient plasma,and then labeled with a detector antibody. The darker shaded bars (blue)represent the population from normals and the lighter shaded bars(green) are from colon cancer patients. (B) shows a normalized graph foreach of the histograms shown in (A). (C) depicts a histogram ofintensity values collected from a multiplexing experiment where beadswhere functionalized with CD66 antibody (the capture antibody),incubated with vesicles purified from patient plasma, and then labeledwith a PE conjugated EpCam antibody (the detector antibody). The redpopulation is from 6 normals and the green is from 21 colon cancerpatients. Data from each individual was normalized to account forvariation in the number of beads detected, added together, and thennormalized again to account for the different number of samples in eachpopulation.

FIG. 70 illustrates multiple detectors can increase the signal. (A)Median intensity values are plotted as a function of purifiedconcentration from the VCaP cell line when labeled with a variety ofprostate specific PE conjugated antibodies. Vesicles captured with EpCam(left graphs) or PCSA (right graphs) and the various proteins detectedby the detector antibody are listed to the right of each graph. In bothcases the combination of CD9 and CD63 gives the best increase in signalover background (bottom graphs depicting percent increase). Thecombination of CD9 and CD63 gave about 200% percent increase overbackground. (B) further illustrates prostate cancer/prostatevesicle-specific marker multiplexing improves detection of prostatecancer cell derived vesicles. Median intensity values are plotted as afunction of purified concentration from the VCaP cell line when labeledwith a variety of prostate specific PE conjugated antibodies. Vesiclescaptured with PCSA (left) and vesicles captured with EpCam (right) aredepicted. In both cases the combination of B7H3 and PSMA gives the bestincrease in signal over background.

FIG. 71 illustrates a colon cancer biosignature for colon cancer bystage, using CD63 detector and CD63 capture. The histograms ofintensities from vesicles captured with CD63 coated beads and labeledwith CD63 conjugated PE. There are 6 patients in the control group (A),4 in stage I (B), 5 in stage II (C), 8 in stage III (D), and 4 stage IV(E). Data from each individual was normalized to account for variationin the number of beads detected, added together, and then normalizedagain to account for the different number of samples in each population(F).

FIG. 72 illustrates colon cancer biosignature for colon cancer by stage,using EpCam detector and CD9 capture. The histograms of intensities arefrom vesicles captured with CD9 coated beads and labeled with EpCam.There are patients in the (A) control group, (B) stage I, (C) stage II,(D) stage III, and (E) stage IV. Data from each individual wasnormalized to account for variation in the number of beads detected,added together, and then normalized again to account for the differentnumber of samples in each population (F).

FIG. 73 illustrates (A) the sensitivity and specificity, and theconfidence level, for detecting prostate cancer using antibodies to thelisted proteins listed as the detector and capture antibodies. CD63,CD9, and CD81 are general markers and EpCam is a cancer marker. Theindividual results are depicted in (B) for EpCam versus CD63, with 99%confidence, 100% (n=8) cancer patient samples were different from theGeneralized Normal Distribution and with 99% confidence, 77% (n=10)normal patient samples were not different from the Generalized NormalDistribution; (C) for CD81 versus CD63, with 99% confidence, 90% (n=5)cancer patient samples were different from the Generalized NormalDistribution; with 99% confidence, 77% (n=10) normal patient sampleswere not different from the Generalized Normal Distribution; (D) forCD63 versus CD63, with 99% confidence, 60% (n=5) cancer patient sampleswere different from the Generalized Normal Distribution; with 99%confidence, 80% (n=10) normal patient samples were not different fromthe Generalized Normal Distribution; (E) for CD9 versus CD63, with 99%confidence, 90% (n=5) cancer patient samples were different from theGeneralized Normal Distribution; with 99% confidence, 77% (n=10) normalpatient samples were not different from the Generalized NormalDistribution.

FIG. 74 illustrates (A) the sensitivity and the confidence level fordetecting colon cancer using antibodies to the listed proteins listed asthe detector and capture antibodies. CD63, CD9 are general markers,EpCam is a cancer marker, and CD66 is a colon marker. The individualresults are depicted in (B) for EpCam versus CD63, with 99% confidence,95% (n=20) cancer patient samples were different from the GeneralizedNormal Distribution; with 99% confidence, 100% (n=6) normal patientsamples were not different from the Generalized Normal Distribution; (C)for EpCam versus CD9, with 99% confidence, 90% (n=20) cancer patientsamples were different from the Generalized Normal Distribution; with99% confidence, 77% (n=6) normal patient samples were not different fromthe Generalized Normal Distribution; (D) for CD63 versus CD63, with 99%confidence, 60% (n=20) cancer patient samples were different from theGeneralized Normal Distribution; with 99% confidence, 80% (n=6) normalpatient samples were not different from the Generalized NormalDistribution; (E) for CD9 versus CD63, with 99% confidence, 90% (n=20)cancer patient samples were different from the Generalized NormalDistribution; with 99% confidence, 77% (n=6) normal patient samples werenot different from the Generalized Normal Distribution; (F) for CD66versus CD9, with 99% confidence, 90% (n=20) cancer patient samples weredifferent from the Generalized Normal Distribution; with 99% confidence,77% (n=6) normal patient samples were not different from the GeneralizedNormal Distribution.

FIG. 75 illustrates the capture of prostate cancer cells-derivedvesicles from plasma with EpCam by assessing TMPRSS2-ERG expression. (A)Graduated amounts of VCAP purified vesicles were spiked into normalplasma. Vesicles were isolated using Dynal beads with either EPCAMantibody or its isotype control. RNA from the vesicles was isolated andthe expression of the TMPRSS2:ERG fusion transcript was measured usingqRT-PCR. (B) VCaP purified vesicles were spiked into normal plasma andthen incubated with Dynal magnetic beads coated with either the EpCam orisotype control antibody. RNA was isolated directly from the Dynalbeads. Equal volumes of RNA from each sample were used for RT-PCR andsubsequent Taqman assays. (C) Cycle threshold (CT) differences of theSPINK1 and GAPDH transcripts between 22RV1 vesicles captured with EpCamand IgG2 isotype negative control beads. Higher CT values indicate lowertranscript expression.

FIG. 76 illustrates the top ten differentially expressed microRNAsbetween VCaP prostate cancer cell derived vesicles and normal plasmavesicles. VCAP cell line vesicles and vesicles from normal plasma wereisolated via ultracentrifugation followed by RNA isolation. MicroRNAswere profiled using qRT-PCR analysis. Prostate cancer cell line derivedvesicles have higher levels (lower CT values) of the indicated microRNAsas depicted in the bar graph.

FIG. 77 depicts a bar graph of miR-21 expression with CD9 bead capture.1 ml of plasma from prostate cancer patients, 250 ng/ml of LNCaP, ornormal purified vesicles were incubated with CD9 coated Dynal beads. TheRNA was isolated from the beads and the bead supernatant. One sample(#6) was also uncaptured for comparison. MiR-21 expression was measuredwith qRT-PCR and the mean CT values for each sample compared. CD9capture improves the detection of miR-21 in prostate cancer samples.

FIG. 78 depicts a bar graph of miR-141 expression with CD9 bead capture.The experiment was performed as in FIG. 77, with miR-141 expressionmeasured with qRT-PCR instead of miR-21.

FIG. 79 represents graphs showing detection of biomarkers CD9, CD81, andCD63 (A-D) or B7H3 and EpCam (E-H) with captures agents for CD9, CD63,CD81, PSMA, PCSA, B7H3, and EpCam for vesicles isolated from a sample(#126) using a 500 μl column with a 100 kDa MWCO (Millipore, Billerica,Mass.) (A, E), 7 ml column with a 150 kDa MWCO (Pierce®, Rockford, Ill.)(B, F), 15 ml column with a 100 kDa MWCO (Millipore, Billerica, Mass.)(C, G), or 20 ml column with a 150 kDa MWCO (Pierce®, Rockford, Ill.)(D, H).

FIG. 80 represents graphs showing detection of biomarkers CD9, CD81, andCD63 (A-D) or B7H3 and EpCam (E-H) with captures agents for CD9, CD63,CD81, PSMA, PCSA, B7H3, and EpCam for vesicles isolated from a sample(#342) using a 500 μl column with a 100 kDa MWCO (Millipore, Billerica,Mass.) (A, E), 7 ml column with a 150 kDa MWCO (Pierce®, Rockford, Ill.)(B, F), 15 ml column with a 100 kDa MWCO (Millipore, Billerica, Mass.)(C, G), or 20 ml column with a 150 kDa MWCO (Pierce®, Rockford, Ill.)(D, H).

FIG. 81 represents graphs showing detection of biomarkers CD9, CD81, andCD63 of vesicles with captures agents for CD9, CD63, CD81, PSMA, PCSA,B7H3, and EpCam from a sample (#126) (A-C) versus another sample (#117)(D-F) using a 7 ml column with a 150 kDa MWCO (Pierce®, Rockford, Ill.)(A, D), 15 ml column with a 100 kDa MWCO (Millipore, Billerica, Mass.)(B, E), or 20 ml column with a 150 kDa MWCO (Pierce®, Rockford, Ill.)(C, F).

FIG. 82 represents graphs showing detection of biomarkers CD9, CD63, andCD81 with the capture agent of A) CD9, B) PCSA, C) PSMA, and D) EpCam.The vesicles were isolated from control samples (healthy samples) andprostate cancer samples, Stage II prostate cancer (PCa) samples. Thereis improved separation between the PCa and controls with thecolumn-based filtration method of isolation as compared toultracentrifugation isolation of vesicles.

FIG. 83 depicts the comparison of the detection level of variousbiomarkers of vesicles isolated from a patient sample (#126) usingultracentrifugation versus a filter based method using a 500 μl columnwith a 100 kDa molecular weight cut off (MWCO) (Millipore, Billerica,Mass.). The graphs depict A) ultracentrifugation purified sample; B)Microcon sample C) ultracentrifugation purified sample and 10 ug Vcapand D) Microcon sample with 10 ug Vcap. The captures agents used areCD9, CD63, CD81, PSMA, PCSA, B7H3, and EpCam, and CD9, CD81, and CD 63detected.

FIG. 84 depicts the comparison of the detection level of variousbiomarkers of vesicles isolated from a patient sample (#342) usingultracentrifugation versus a filter based method using a 500 μl columnwith a 100 kDa MWCO (Millipore, Billerica, Mass.). The graphs depict A)ultracentrifugation purified sample; B) Microcon sample C)ultracentrifugation purified sample and 10 ug Vcap and D) Microconsample with 10 ug Vcap. The capture agents used are CD9, CD63, CD81,PSMA, PCSA, B7H3, and EpCam, and CD9, CD81, and CD 63 detected.

FIG. 85 illustrates separation and identification of vesicles using theMoFlo XDP.

FIGS. 86A-86D illustrate flow sorting of vesicles in plasma. FIG. 86Ashows detection and sorting of PCSA positive vesicles in the plasma ofprostate cancer patients. FIG. 86B shows detection and sorting of CD45positive vesicles in the plasma of normal and prostate cancer patients.FIG. 86C shows detection and sorting of CD45 positive vesicles in theplasma of normal and breast cancer patients. FIG. 86D shows detectionand sorting of DLL4 positive vesicles in the plasma of normal andprostate cancer patients.

FIG. 87 represents a schematic of detecting vesicles in a sample whereinthe presence or level of the desired vesicles are assessed using amicrosphere platform. FIG. 87A represents a schematic of isolatingvesicles from plasma using a column based filtering method, wherein theisolated vesicles are subsequently assessed using a microsphereplatform. FIG. 87B represents a schematic of compression of a membraneof a vesicle due to high-speed centrifugation, such asultracentrifugation. FIG. 87C represents a schematic of detectingvesicles bound to microspheres using laser detection.

FIG. 88A illustrates the ability of a vesicle bio-signature todiscriminate between normal prostate and PCa samples. Cancer markersincluded EpCam and B7H3. General vesicle markers included CD9, CD81 andCD63. Prostate specific markers included PCSA. The test was found to be98% sensitive and 95% specific for PCa vs normal samples. FIG. 88Billustrates mean fluorescence intensity (MFI) on the Y axis for vesiclemarkers of FIG. 88A in normal and prostate cancer patients.

FIG. 89A illustrates improved sensitivity of the vesicle assays of theinvention versus conventional PCa testing. FIG. 89B illustrates improvedspecificity of the vesicle assays of the invention versus conventionalPCa testing.

FIG. 90 illustrates discrimination of BPH samples from normals and PCasamples using CD63.

FIG. 91 illustrates the ability of a vesicle biosignature todiscriminate between normal prostate and PCa samples. Cancer markersincluded EpCam and B7H3. General vesicle markers included CD9, CD81 andCD63. Prostate specific markers included PCSA. The test was found to be98% sensitive and 84% specific for PCa vs normal & BPH samples.

FIG. 92 illustrates improved specificity of the vesicle assays of theinvention for PCa versus conventional testing even when BPH samples areincluded.

FIG. 93 illustrates ROC curve analysis of the vesicle assays of theinvention versus conventional testing.

FIG. 94 illustrates a correlation between general vesicle (e.g. vesicle“MV”) levels, levels of prostate-specific MVs and MVs with cancermarkers.

FIG. 95 illustrates vesicle markers that distinguish between PCa andnormal samples.

FIG. 96 is a schematic for A) a vesicle prostate cancer assay, whichleads to a decision tree (B), C), D)) for determining whether a sampleis positive for prostate cancer.

FIG. 97A shows the results of a vesicle detection assay for prostatecancer following the decision tree versus detection using elevated PSAlevels. FIG. 97B shows the results of a vesicle detection assay forprostate cancer following the decision tree on a cohort of 933 PCa andnon-PCa patient samples. FIG. 97C shows an ROC curve corresponding tothe data shown in FIG. 97B.

FIG. 98 illustrates the use of cluster analysis to set the MFI thresholdfor vesicle biomarkers of prostate cancer. A) Raw and log transformeddata for 149 samples. The raw data is plotted in the left column and thetransformed data in the right. B) Cluster analysis on PSMA vs B7H3 usinglog transformed data as input. The circles (normals) and x's (cancer)show the two clusters found. The open large circles show the point thatwas used as the center of the cluster. Blue lines show the chosen cutofffor each parameter. C) Cluster analysis on PCSA vs B7H3 using logtransformed data as input. The circles (normals) and x's (cancer) showthe two clusters found. The open large circles show the point that wasused as the center of the cluster. Blue lines show the chosen cutoff foreach parameter. D) Cluster analysis on PSMA vs PCSA using logtransformed data as input. The circles and x's show the two clustersfound. The open large red circles show the point that was used as thecenter of the cluster. Blue lines show the chosen cutoff for eachparameter. E) The thresholds determined in B-D) were applied to thelarger set of data containing 313 samples, and resulted in a sensitivityof 92.8% and a specificity of 78.7%.

FIG. 99 illustrates mean fluorescence intensity (MFI) on the y-axis forassessing vesicles in prostate cancer (Cancer) and normal (Normal)samples. Vesicle protein biomarkers are indicated on the x-axis,including from left to right CD9, PSMA, PCSA, CD63, CD81, B7H3, IL-6,OPG-13 (also referred to as OPG), IL6R, PA2G4, EZH2, RUNX2, SERPINB3 andEpCam.

FIG. 100 illustrates differentiation of BPH vs stage III PCa usingantibody arrays.

FIG. 101 illustrates levels of miR-145 in vesicles isolated from controland PCa samples.

FIGS. 102A-102B illustrate levels of miR-107 (FIG. 102A) and miR-574-3p(FIG. 102B) in vesicles isolated from control (non PCa) and prostatecancer samples, as indicated on the X axis. miRs were detected inisolated vesicles using Taqman assays. P values are shown below theplot. The Y axis shows copy number of miRs detected. In FIG. 102B, twooutlier samples from each sample group with copy numbers well outsidethe deviation of the samples were excluded from analysis.

FIGS. 103A-103D illustrate levels of miR-141 (FIG. 103A), miR-375 (FIG.103B), miR-200b (FIG. 103C) and miR-574-3p (FIG. 103D) in vesiclesisolated from metastatic (M1) and non-metastatic (M0) prostate cancersamples. miRs were detected in isolated vesicles using Taqman assays.

FIGS. 104A-104B illustrate the use of miR-107 and miR-141 to identifyfalse negatives from a vesicle-based diagnostic assay for prostatecancer. FIG. 104A illustrates a scheme for using miR analysis withinvesicles to convert false negatives into true positives, therebyimproving sensitivity. FIG. 104B illustrates a scheme for using miRanalysis within vesicles to convert false positives into true negatives,thereby improving specificity. Normalized levels of miR-107 (FIG. 104C)and miR-141 (FIG. 104D) are shown on the Y axis for true positives (TP)called by the vesicle diagnostic assay, true negatives (TN) called bythe vesicle diagnostic assay, false positives (FP) called by the vesiclediagnostic assay, and false negatives (FN) called by the vesiclediagnostic assay.

FIGS. 105A-105F illustrate box plots of the elevation of hsa-miR-432(FIG. 105A), hsa-miR-143 (FIG. 105B), hsa-miR-424 (FIG. 105C),hsa-miR-204 (FIG. 105D), hsa-miR-581f (FIG. 105E) and hsa-miR-451 (FIG.105F) in patients with or without PCa and PSA ≧ or <4.0 ng/ml. miRs weredetected in isolated vesicles using Taqman assays. Levels of miRsdetected by Taqman assays are displayed on the Y axis. The X axis showsfour groups of samples. From left to right, “Control no” are controlpatients with PSA ≧4.0; “Control yes” are control patients with PSA<4.0; “Diseased no” are prostate cancer patients with PSA ≧4.0; and“Diseased yes” are prostate cancer patients with PSA <4.0.

FIG. 106 illustrates the levels of microRNAs miR-29a and miR-145 invesicles isolated from plasma samples from prostate cancer (PCa) andcontrols.

FIG. 107 illustrates a plate layout for microbead assays.

FIGS. 108A-D illustrate the ability of various capture antibodies usedto capture vesicles that distinguish colorectal cancer (CRC) versusnormal samples. FIG. 108A illustrates a fold-change (Y-axis) in captureantibody antigens (X-axis) in CRC vesicle samples versus normals asmeasured by antibody array. FIG. 108B is similar except that the Y-axisrepresents the median fluorescence intensity (MFI) in CRC and normalsamples as indicated by the legend. FIG. 108C is similar to FIG. 108Bperformed on an additional sample set. FIG. 108D shows analysis usingCD24 is used as a colon marker, TROP2 as a cancer marker, and thetetraspanins CD9, CD63 and CD81 as general vesicle markers.

FIGS. 109A-H illustrate detection of CRC in plasma samples by detectingvesicles using TMEM211 and/or CD24. FIG. 109A illustrates ROC curveanalysis of the vesicle assays of the invention with the biomarkerTMEM211. FIG. 109B illustrates ROC curve analysis of the vesicle assaysof the invention with the biomarker CD24. FIG. 109C illustrates analysisof the vesicle assays of the invention for normals, subjects withcolorectal cancer (CRC), and confounders. FIG. 109D illustrates analysisof vesicle samples in a follow on study using biomarker TMEM211 fornormals, subjects with colorectal cancer (CRC), and confounders. FIG.109E illustrates ROC curve analysis of the vesicle assays of theinvention with the biomarker TMEM211. FIG. 109F-109H illustrate theresults from an additional study with an expanded patient cohort. InFIG. 109F, median fluorescence intensity (MFI) for TMEM211 is shown onthe X axis and MFI for CD24 is shown on the Y axis. Results for TMEM211and CD24 to distinguish various classes of samples individually areshown in FIG. 109G and FIG. 109H, respectively.

FIG. 110 illustrates TaqMan Low Density Array (TLDA) miRNA cardcomparison of colorectal cancer (CRC) cell lines versus normal vesicles.The CRC cell lines are indicated to the right of the plot. The Y-axisshows a fold-change in expression in the CRC cell lines compared tonormal controls. The miRNAs surveyed are indicated on the X-axis, andfrom left to right are miR-548c-5p, miR-362-3p, miR-422a, miR-597,miR-429, miR-200a, and miR-200b. For each miR, the bars from left toright correspond to cell lines LOVO, HT29, SW260, COL0205, HCT116 andRKO. These miRNAs were not overexpressed in normal or melanoma cells.

FIG. 111A illustrates differentiation of normal and CRC samples usingmiR 92 and miR 491. FIG. 111B illustrates differentiation of normal andCRC samples using miR 92 and miR 21. FIG. 111C illustratesdifferentiation of normal and CRC samples using multiplexing with miR92, miR 21, miR 9 and miR 491.

FIG. 112 illustrates KRAS sequencing in a colorectal cancer (CRC) cellline and patient sample. Samples comprise genomic DNA obtained from thecell line (B) or from a tissue sample from the patient (D), or cDNAobtained from RNA payload within vesicles shed from the cell line (A) orfrom a plasma sample from the patient (C).

FIG. 113 illustrates discrimination of CRC by detecting TMEM211 and MUC1in microvesicles from plasma samples. The X axis (MUC1) and Y axis(TMEM211) correspond to the median fluorescence intensity (MFI) of thedetected vesicles in the samples. The horizontal and vertical lines arethe MFI threshold values for detecting CRC for TMEM211 and MUC1,respectively.

FIG. 114A illustrates a graph depicting the fold change over normal ofbiomarkers detected in breast cancer patient samples (n=10) or normalcontrols (i.e., no breast cancer). Vesicles in plasma samples werecaptured with antibodies to the indicated antigens tethered to beads.The captured vesicles were detected with labeled antibodies totetraspanins CD9, CD63 and CD81. The fold change on the Y axis is thefold change median fluorescence intensity (MFI) of the vesicles detectedin the breast cancer samples compared to normal. FIG. 114B illustratesthe level of various biomarkers detected in vesicles derived from breastcancer cell lines MCF7, T47D and MDA. T47D and MDA are metastatic celllines.

FIG. 115A illustrates a fold-change in various biomarkers in membranevesicle from lung cancer samples as compared to normal samples detectedusing antibodies against the indicated vesicle antigens. Black bars arethe ratios of lung cancer samples to normal samples. White bars are theratios of non-lung cancer samples to normal samples. The underlying datais presented in FIG. 115B. FIG. 115B illustrates fluorescence levels ofmembrane vesicles detected using antibodies against the indicatedvesicle antigens. Fluorescence levels are averages from the followingsamples: normals (white), non-lung cancer samples (grey) and staged lungcancer samples (black). FIG. 115C shows the median fluorescenceintensity (MFI) of vesicles detecting using EPHA2 (i), CD24 (ii), EGFR(iii), and CEA (iv) in samples from lung cancer patients and normalcontrols. FIG. 115D and FIG. 115E present plots of mean fluorescenceintensity (MFI) on the Y axis for vesicles detected in samples from lungcancer and normal (non-lung cancer) subjects. Capture antibodies areindicated along the X axis.

FIG. 116 presents a decision tree for detecting lung cancer using theindicated capture antibodies to detect vesicles.

FIG. 117A illustrates CD81 labeled vesicle level vs circulating tumorcells (CTCs) in plasma derived vesicles. Vesicles collected from patient(14 leftmost “CTC” samples) and normal plasma (four rightmost samples)had vesicle levels measured with CD81 and CTCs counted. FIG. 117Billustrates miR-21 copy number vs CTCs in EpCAM+ plasma derivedvesicles. Patient samples (15 leftmost “CTC” samples) and normal samples(seven rightmost “Normal” samples) are indicated. Copy number wasassessed by qRT-PCR of miR-21 from RNA extracted from EpCAM+plasmaderived vesicles. CTC counts were obtained from the same samples.

FIGS. 118A-118C illustrate the levels of vesicles in plasma from abreast cancer patient detected using antibodies to CD31 (FIG. 118A),DLL4 (FIG. 118B) and CD9 (FIG. 118C) after depletion of CD31+ positivevesicles from the sample.

FIG. 119 illustrates detection of Tissue Factor (TF) in vesicles fromnormal (non-cancer) plasma samples, breast cancer (BCa) plasma samplesand prostate cancer (PCa) plasma samples. Vesicles in plasma sampleswere captured with anti-Tissue Factor antibodies tethered tomicrospheres. The captured vesicles were detected with labeledantibodies to tetraspanins CD9, CD63 and CD81.

FIGS. 120A-C shows epitope mapping using an anti-TMEM211 rabbitpolyclonal antibody. The antibody was tested against a series ofoverlapping peptides from TMEM211. FIG. 120A shows binding to thepeptides with the anti-TMEM211 rabbit polyclonal antibody and goatanti-rabbit IgG HRP secondary antibody. FIG. 120B shows binding to thepeptides with a control rabbit polyclonal antibody and goat anti-rabbitIgG HRP secondary antibody. FIG. 120C shows results of binding to thepeptides with the anti-TMEM211 rabbit polyclonal antibody and goatanti-mouse IgG HRP secondary antibody.

FIGS. 121A-C shows epitope mapping using an anti-B7H3 (B7-H3) ratmonoclonal antibody. The antibody was tested against a series ofoverlapping peptides from B7H3. FIG. 121A shows binding to the peptideswith the anti-B7H3 rat monoclonal antibody and goat anti-rat IgG HRPsecondary antibody. FIG. 121B shows binding to the peptides with acontrol rat polyclonal antibody and goat anti-rat IgG HRP secondaryantibody. FIG. 121C shows results of binding to the peptides with theanti-B7H3 rat monoclonal antibody and goat anti-rabbit IgG HRP secondaryantibody.

FIGS. 122A-B show screening of the output phage from panning using phageELISA. A phage library was panned against a target anti-human CD9 mousemonoclonal antibody. The output phage from three rounds of panning werescreened with the target antibody (FIG. 122A) or an anti-mouse IgGcontrol antibody (FIG. 123B).

DETAILED DESCRIPTION OF THE INVENTION

Disclosed herein are methods and systems for characterizing a phenotypeof a biological sample, e.g., a sample from a cell culture, an organism,or a subject. The phenotype can be characterized by assessing one ormore biomarkers. The biomarkers can be associated with a vesicle orvesicle population, either presented vesicle surface antigens or vesiclepayload. As used herein, vesicle payload comprises entities encapsulatedwithin a vesicle. Vesicle associated biomarkers can comprise bothmembrane bound and soluble biomarkers. The biomarkers can also becirculating biomarkers, such as microRNA or protein assessed in a bodilyfluid. Unless otherwise specified, the terms “purified” or “isolated” asused herein in reference to vesicles or biomarker components meanpartial or complete purification or isolation of such components from acell or organism. Furthermore, unless otherwise specified, reference tovesicle isolation using a binding agent includes binding a vesicle withthe binding agent whether or not such binding results in completeisolation of the vesicle apart from other biological entities in thestarting material.

A method of characterizing a phenotype by analyzing a circulatingbiomarker, e.g., a nucleic acid biomarker, is depicted in scheme 6100Aof FIG. 61A, as a non-limiting illustrative example. In a first step6101, a biological sample is obtained, e.g., a bodily fluid, tissuesample or cell culture. Nucleic acids are isolated from the sample 6103.The nucleic acid can be DNA or RNA, e.g., microRNA. Assessment of suchnucleic acids can provide a biosignature for a phenotype. By samplingthe nucleic acids associated with target phenotype (e.g., disease versushealthy, pre- and post-treatment), one or more nucleic acid markers thatare indicative of the phenotype can be determined. Various aspects ofthe present invention are directed to biosignatures determined byassessing one or more nucleic acid molecules (e.g., microRNA) present inthe sample 6105, where the biosignature corresponds to a predeterminedphenotype 6107. FIG. 61B illustrates a scheme 6100B of using vesicles toisolate the nucleic acid molecules. In one example, a biological sampleis obtained 6102, and one or more vesicles, e.g., vesicles from aparticular cell-of-origin and/or vesicles associated with a particulardisease state, are isolated from the sample 6104. The vesicles areanalyzed 6106 by characterizing surface antigens associated with thevesicles and/or determining the presence or levels of components presentwithin the vesicles (“payload”). Unless specified otherwise, the term“antigen” as used herein refers generally to a biomarker that can bebound by a binding agent, whether the binding agent is an antibody,aptamer, lectin, or other binding agent for the biomarker and regardlessof whether such biomarker illicits an immune response in a host. Vesiclepayload may be protein, including peptides and polypeptides, and/ornucleic acids such as DNA and RNAs. RNA payload includes messenger RNA(mRNA) and microRNA (also referred to herein as miRNA or miR). Aphenotype is characterized based on the biosignature of the vesicles6108. In another illustrative method of the invention, schemes 6100A and6100B are performed together to characterize a phenotype. In such ascheme, vesicles and nucleic acids, e.g., microRNA, are assessed,thereby characterizing the phenotype.

In a related aspect, methods are provided herein for the discovery ofbiomarkers comprising assessing vesicle surface markers or payloadmarkers in one sample and comparing the markers to another sample.Markers that distinguish between the samples can be used as biomarkersaccording to the invention. Such samples can be from a subject or groupof subjects. For example, the groups can be, e.g., known responders andnon-responders to a given treatment for a given disease or disorder.Biomarkers discovered to distinguish the known responders andnon-responders provide a biosignature of whether a subject is likely torespond to a treatment such as a therapeutic agent, e.g., a drug orbiologic.

Phenotypes

Disclosed herein are products and processes for characterizing aphenotype of an individual by analyzing a vesicle such as a membranevesicle. A phenotype can be any observable characteristic or trait of asubject, such as a disease or condition, a disease stage or conditionstage, susceptibility to a disease or condition, prognosis of a diseasestage or condition, a physiological state, or response to therapeutics.A phenotype can result from a subject's gene expression as well as theinfluence of environmental factors and the interactions between the two,as well as from epigenetic modifications to nucleic acid sequences.

A phenotype in a subject can be characterized by obtaining a biologicalsample from a subject and analyzing one or more vesicles from thesample. For example, characterizing a phenotype for a subject orindividual may include detecting a disease or condition (includingpre-symptomatic early stage detecting), determining the prognosis,diagnosis, or theranosis of a disease or condition, or determining thestage or progression of a disease or condition. Characterizing aphenotype can also include identifying appropriate treatments ortreatment efficacy for specific diseases, conditions, disease stages andcondition stages, predictions and likelihood analysis of diseaseprogression, particularly disease recurrence, metastatic spread ordisease relapse. A phenotype can also be a clinically distinct type orsubtype of a condition or disease, such as a cancer or tumor. Phenotypedetermination can also be a determination of a physiological condition,or an assessment of organ distress or organ rejection, such aspost-transplantation. The products and processes described herein allowassessment of a subject on an individual basis, which can providebenefits of more efficient and economical decisions in treatment.

In an aspect, the invention relates to the analysis of vesicles toprovide a biosignature to predict whether a subject is likely to respondto a treatment for a disease or disorder. Characterizating a phenotypeincludes predicting the responder/non-responder status of the subject,wherein a responder responds to a treatment for a disease and anon-responder does not respond to the treatment. Vesicles can beanalyzed in the subject and compared to vesicle analysis of previoussubjects that were known to respond or not to a treatment. If thevesicle biosignature in a subject more closely aligns with that ofprevious subjects that were known to respond to the treatment, thesubject can be characterized, or predicted, as a responder to thetreatment. Similarly, if the vesicle biosignature in the subject moreclosely aligns with that of previous subjects that did not respond tothe treatment, the subject can be characterized, or predicted as anon-responder to the treatment. The treatment can be for any appropriatedisease, disorder or other condition. The method can be used in anydisease setting where a vesicle biosignature that correlates withresponder/non-responder status is known.

The term “phenotype” as used herein can mean any trait or characteristicthat is attributed to a vesicle biosignature that is identifiedutilizing methods of the invention. For example, a phenotype can be theidentification of a subject as likely to respond to a treatment, or morebroadly, it can be a diagnostic, prognostic or theranostic determinationbased on a characterized biosignature for a sample obtained from asubject.

In some embodiments, the phenotype comprises a disease or condition suchas those listed in Table 1. For example, the phenotype can comprise thepresence of or likelihood of developing a tumor, neoplasm, or cancer. Acancer detected or assessed by products or processes described hereinincludes, but is not limited to, breast cancer, ovarian cancer, lungcancer, colon cancer, hyperplastic polyp, adenoma, colorectal cancer,high grade dysplasia, low grade dysplasia, prostatic hyperplasia,prostate cancer, melanoma, pancreatic cancer, brain cancer (such as aglioblastoma), hematological malignancy, hepatocellular carcinoma,cervical cancer, endometrial cancer, head and neck cancer, esophagealcancer, gastrointestinal stromal tumor (GIST), renal cell carcinoma(RCC) or gastric cancer. The colorectal cancer can be CRC Dukes B orDukes C-D. The hematological malignancy can be B-Cell ChronicLymphocytic Leukemia, B-Cell Lymphoma-DLBCL, B-CellLymphoma-DLBCL-germinal center-like, B-Cell Lymphoma-DLBCL-activatedB-cell-like, and Burkitt's lymphoma.

The phenotype can be a premalignant condition, such as actinickeratosis, atrophic gastritis, leukoplakia, erythroplasia, LymphomatoidGranulomatosis, preleukemia, fibrosis, cervical dysplasia, uterinecervical dysplasia, xeroderma pigmentosum, Barrett's Esophagus,colorectal polyp, or other abnormal tissue growth or lesion that islikely to develop into a malignant tumor. Transformative viralinfections such as HIV and HPV also present phenotypes that can beassessed according to the invention.

The cancer characterized by the methods of the invention can comprise,without limitation, a carcinoma, a sarcoma, a lymphoma or leukemia, agerm cell tumor, a blastoma, or other cancers. Carcinomas includewithout limitation epithelial neoplasms, squamous cell neoplasmssquamous cell carcinoma, basal cell neoplasms basal cell carcinoma,transitional cell papillomas and carcinomas, adenomas andadenocarcinomas (glands), adenoma, adenocarcinoma, linitis plasticainsulinoma, glucagonoma, gastrinoma, vipoma, cholangiocarcinoma,hepatocellular carcinoma, adenoid cystic carcinoma, carcinoid tumor ofappendix, prolactinoma, oncocytoma, hurthle cell adenoma, renal cellcarcinoma, grawitz tumor, multiple endocrine adenomas, endometrioidadenoma, adnexal and skin appendage neoplasms, mucoepidermoid neoplasms,cystic, mucinous and serous neoplasms, cystadenoma, pseudomyxomaperitonei, ductal, lobular and medullary neoplasms, acinar cellneoplasms, complex epithelial neoplasms, warthin's tumor, thymoma,specialized gonadal neoplasms, sex cord stromal tumor, thecoma,granulosa cell tumor, arrhenoblastoma, sertoli leydig cell tumor, glomustumors, paraganglioma, pheochromocytoma, glomus tumor, nevi andmelanomas, melanocytic nevus, malignant melanoma, melanoma, nodularmelanoma, dysplastic nevus, lentigo maligna melanoma, superficialspreading melanoma, and malignant acral lentiginous melanoma. Sarcomaincludes without limitation Askin's tumor, botryodies, chondrosarcoma,Ewing's sarcoma, malignant hemangio endothelioma, malignant schwannoma,osteosarcoma, soft tissue sarcomas including: alveolar soft partsarcoma, angiosarcoma, cystosarcoma phyllodes, dermatofibrosarcoma,desmoid tumor, desmoplastic small round cell tumor, epithelioid sarcoma,extraskeletal chondrosarcoma, extraskeletal osteosarcoma, fibrosarcoma,hemangiopericytoma, hemangiosarcoma, kaposi's sarcoma, leiomyosarcoma,liposarcoma, lymphangiosarcoma, lymphosarcoma, malignant fibroushistiocytoma, neurofibrosarcoma, rhabdomyosarcoma, and synovialsarcoma.Lymphoma and leukemia include without limitation chronic lymphocyticleukemia/small lymphocytic lymphoma, B-cell prolymphocytic leukemia,lymphoplasmacytic lymphoma (such as waldenstrom macroglobulinemia),splenic marginal zone lymphoma, plasma cell myeloma, plasmacytoma,monoclonal immunoglobulin deposition diseases, heavy chain diseases,extranodal marginal zone B cell lymphoma, also called malt lymphoma,nodal marginal zone B cell lymphoma (nmzl), follicular lymphoma, mantlecell lymphoma, diffuse large B cell lymphoma, mediastinal (thymic) largeB cell lymphoma, intravascular large B cell lymphoma, primary effusionlymphoma, burkitt lymphoma/leukemia, T cell prolymphocytic leukemia, Tcell large granular lymphocytic leukemia, aggressive NK cell leukemia,adult T cell leukemia/lymphoma, extranodal NK/T cell lymphoma, nasaltype, enteropathy-type T cell lymphoma, hepatosplenic T cell lymphoma,blastic NK cell lymphoma, mycosis fungoides/sezary syndrome, primarycutaneous CD30-positive T cell lymphoproliferative disorders, primarycutaneous anaplastic large cell lymphoma, lymphomatoid papulosis,angioimmunoblastic T cell lymphoma, peripheral T cell lymphoma,unspecified, anaplastic large cell lymphoma, classical hodgkin lymphomas(nodular sclerosis, mixed cellularity, lymphocyte-rich, lymphocytedepleted or not depleted), and nodular lymphocyte-predominant hodgkinlymphoma. Germ cell tumors include without limitation germinoma,dysgerminoma, seminoma, nongerminomatous germ cell tumor, embryonalcarcinoma, endodermal sinus turmor, choriocarcinoma, teratoma,polyembryoma, and gonadoblastoma. Blastoma includes without limitationnephroblastoma, medulloblastoma, and retinoblastoma. Other cancersinclude without limitation labial carcinoma, larynx carcinoma,hypopharynx carcinoma, tongue carcinoma, salivary gland carcinoma,gastric carcinoma, adenocarcinoma, thyroid cancer (medullary andpapillary thyroid carcinoma), renal carcinoma, kidney parenchymacarcinoma, cervix carcinoma, uterine corpus carcinoma, endometriumcarcinoma, chorion carcinoma, testis carcinoma, urinary carcinoma,melanoma, brain tumors such as glioblastoma, astrocytoma, meningioma,medulloblastoma and peripheral neuroectodermal tumors, gall bladdercarcinoma, bronchial carcinoma, multiple myeloma, basalioma, teratoma,retinoblastoma, choroidea melanoma, seminoma, rhabdomyosarcoma,craniopharyngeoma, osteosarcoma, chondrosarcoma, myosarcoma,liposarcoma, fibrosarcoma, Ewing sarcoma, and plasmocytoma.

In a further embodiment, the cancer under analysis may be a lung cancerincluding non-small cell lung cancer and small cell lung cancer(including small cell carcinoma (oat cell cancer), mixed smallcell/large cell carcinoma, and combined small cell carcinoma), coloncancer, breast cancer, prostate cancer, liver cancer, pancreas cancer,brain cancer, kidney cancer, ovarian cancer, stomach cancer, skincancer, bone cancer, gastric cancer, breast cancer, pancreatic cancer,glioma, glioblastoma, hepatocellular carcinoma, papillary renalcarcinoma, head and neck squamous cell carcinoma, leukemia, lymphoma,myeloma, or a solid tumor.

In embodiments, the cancer comprises an acute lymphoblastic leukemia;acute myeloid leukemia; adrenocortical carcinoma; AIDS-related cancers;AIDS-related lymphoma; anal cancer; appendix cancer; astrocytomas;atypical teratoid/rhabdoid tumor; basal cell carcinoma; bladder cancer;brain stem glioma; brain tumor (including brain stem glioma, centralnervous system atypical teratoid/rhabdoid tumor, central nervous systemembryonal tumors, astrocytomas, craniopharyngioma, ependymoblastoma,ependymoma, medulloblastoma, medulloepithelioma, pineal parenchymaltumors of intermediate differentiation, supratentorial primitiveneuroectodermal tumors and pineoblastoma); breast cancer; bronchialtumors; Burkitt lymphoma; cancer of unknown primary site; carcinoidtumor; carcinoma of unknown primary site; central nervous systematypical teratoid/rhabdoid tumor; central nervous system embryonaltumors; cervical cancer; childhood cancers; chordoma; chroniclymphocytic leukemia; chronic myelogenous leukemia; chronicmyeloproliferative disorders; colon cancer; colorectal cancer;craniopharyngioma; cutaneous T-cell lymphoma; endocrine pancreas isletcell tumors; endometrial cancer; ependymoblastoma; ependymoma;esophageal cancer; esthesioneuroblastoma; Ewing sarcoma; extracranialgerm cell tumor; extragonadal germ cell tumor; extrahepatic bile ductcancer; gallbladder cancer; gastric (stomach) cancer; gastrointestinalcarcinoid tumor; gastrointestinal stromal cell tumor; gastrointestinalstromal tumor (GIST); gestational trophoblastic tumor; glioma; hairycell leukemia; head and neck cancer; heart cancer; Hodgkin lymphoma;hypopharyngeal cancer; intraocular melanoma; islet cell tumors; Kaposisarcoma; kidney cancer; Langerhans cell histiocytosis; laryngeal cancer;lip cancer; liver cancer; malignant fibrous histiocytoma bone cancer;medulloblastoma; medulloepithelioma; melanoma; Merkel cell carcinoma;Merkel cell skin carcinoma; mesothelioma; metastatic squamous neckcancer with occult primary; mouth cancer; multiple endocrine neoplasiasyndromes; multiple myeloma; multiple myeloma/plasma cell neoplasm;mycosis fungoides; myelodysplastic syndromes; myeloproliferativeneoplasms; nasal cavity cancer; nasopharyngeal cancer; neuroblastoma;Non-Hodgkin lymphoma; nonmelanoma skin cancer; non-small cell lungcancer; oral cancer; oral cavity cancer; oropharyngeal cancer;osteosarcoma; other brain and spinal cord tumors; ovarian cancer;ovarian epithelial cancer; ovarian germ cell tumor; ovarian lowmalignant potential tumor; pancreatic cancer; papillomatosis; paranasalsinus cancer; parathyroid cancer; pelvic cancer; penile cancer;pharyngeal cancer; pineal parenchymal tumors of intermediatedifferentiation; pineoblastoma; pituitary tumor; plasma cellneoplasm/multiple myeloma; pleuropulmonary blastoma; primary centralnervous system (CNS) lymphoma; primary hepatocellular liver cancer;prostate cancer; rectal cancer; renal cancer; renal cell (kidney)cancer; renal cell cancer; respiratory tract cancer; retinoblastoma;rhabdomyosarcoma; salivary gland cancer; Sézary syndrome; small celllung cancer; small intestine cancer; soft tissue sarcoma; squamous cellcarcinoma; squamous neck cancer; stomach (gastric) cancer;supratentorial primitive neuroectodermal tumors; T-cell lymphoma;testicular cancer; throat cancer; thymic carcinoma; thymoma; thyroidcancer; transitional cell cancer; transitional cell cancer of the renalpelvis and ureter; trophoblastic tumor; ureter cancer; urethral cancer;uterine cancer; uterine sarcoma; vaginal cancer; vulvar cancer;Waldenström macroglobulinemia; or Wilm's tumor. The methods of theinvention can be used to characterize these and other cancers. Thus,characterizing a phenotype can be providing a diagnosis, prognosis ortheranosis of one of the cancers disclosed herein.

The phenotype can also be an inflammatory disease, immune disease, orautoimmune disease. For example, the disease may be inflammatory boweldisease (IBD), Crohn's disease (CD), ulcerative colitis (UC), pelvicinflammation, vasculitis, psoriasis, diabetes, autoimmune hepatitis,Multiple Sclerosis, Myasthenia Gravis, Type I diabetes, RheumatoidArthritis, Psoriasis, Systemic Lupus Erythematosis (SLE), Hashimoto'sThyroiditis, Grave's disease, Ankylosing Spondylitis Sjogrens Disease,CREST syndrome, Scleroderma, Rheumatic Disease, organ rejection, PrimarySclerosing Cholangitis, or sepsis.

The phenotype can also comprise a cardiovascular disease, such asatherosclerosis, congestive heart failure, vulnerable plaque, stroke, orischemia. The cardiovascular disease or condition can be high bloodpressure, stenosis, vessel occlusion or a thrombotic event.

The phenotype can also comprise a neurological disease, such as MultipleSclerosis (MS), Parkinson's Disease (PD), Alzheimer's Disease (AD),schizophrenia, bipolar disorder, depression, autism, Prion Disease,Pick's disease, dementia, Huntington disease (HD), Down's syndrome,cerebrovascular disease, Rasmussen's encephalitis, viral meningitis,neurospsychiatric systemic lupus erythematosus (NPSLE), amyotrophiclateral sclerosis, Creutzfeldt-Jacob disease,Gerstmann-Straussler-Scheinker disease, transmissible spongiformencephalopathy, ischemic reperfusion damage (e.g. stroke), brain trauma,microbial infection, or chronic fatigue syndrome. The phenotype may alsobe a condition such as fibromyalgia, chronic neuropathic pain, orperipheral neuropathic pain.

The phenotype may also comprise an infectious disease, such as abacterial, viral or yeast infection. For example, the disease orcondition may be Whipple's Disease, Prion Disease, cirrhosis,methicillin-resistant staphylococcus aureus, HIV, hepatitis, syphilis,meningitis, malaria, tuberculosis, or influenza. Viral proteins, such asHIV or HCV-like particles can be assessed in a vesicle, to characterizea viral condition.

The phenotype can also comprise a perinatal or pregnancy relatedcondition (e.g. preeclampsia or preterm birth), metabolic disease orcondition, such as a metabolic disease or condition associated with ironmetabolism. For example, hepcidin can be assayed in a vesicle tocharacterize an iron deficiency. The metabolic disease or condition canalso be diabetes, inflammation, or a perinatal condition.

The methods of the invention can be used to characterize these and otherdiseases and disorders that can be assessed via biomarkers. Thus,characterizing a phenotype can be providing a diagnosis, prognosis ortheranosis of one of the diseases and disorders disclosed herein.

Subject

One or more phenotypes of a subject can be determined by analyzing oneor more vesicles, such as vesicles, in a biological sample obtained fromthe subject. A subject or patient can include, but is not limited to,mammals such as bovine, avian, canine, equine, feline, ovine, porcine,or primate animals (including humans and non-human primates). A subjectcan also include a mammal of importance due to being endangered, such asa Siberian tiger; or economic importance, such as an animal raised on afarm for consumption by humans, or an animal of social importance tohumans, such as an animal kept as a pet or in a zoo. Examples of suchanimals include, but are not limited to, carnivores such as cats anddogs; swine including pigs, hogs and wild boars; ruminants or ungulatessuch as cattle, oxen, sheep, giraffes, deer, goats, bison, camels orhorses. Also included are birds that are endangered or kept in zoos, aswell as fowl and more particularly domesticated fowl, i.e. poultry, suchas turkeys and chickens, ducks, geese, guinea fowl. Also included aredomesticated swine and horses (including race horses). In addition, anyanimal species connected to commercial activities are also included suchas those animals connected to agriculture and aquaculture and otheractivities in which disease monitoring, diagnosis, and therapy selectionare routine practice in husbandry for economic productivity and/orsafety of the food chain.

The subject can have a pre-existing disease or condition, such ascancer. Alternatively, the subject may not have any known pre-existingcondition. The subject may also be non-responsive to an existing or pasttreatment, such as a treatment for cancer.

Samples

The biological sample obtained from the subject can be any bodily fluid.For example, the biological sample can be peripheral blood, sera,plasma, ascites, urine, cerebrospinal fluid (CSF), sputum, saliva, bonemarrow, synovial fluid, aqueous humor, amniotic fluid, cerumen, breastmilk, broncheoalveolar lavage fluid, semen (including prostatic fluid),Cowper's fluid or pre-ejaculatory fluid, female ejaculate, sweat, fecalmatter, hair, tears, cyst fluid, pleural and peritoneal fluid,pericardial fluid, lymph, chyme, chyle, bile, interstitial fluid,menses, pus, sebum, vomit, vaginal secretions, mucosal secretion, stoolwater, pancreatic juice, lavage fluids from sinus cavities,bronchopulmonary aspirates or other lavage fluids. A biological samplemay also include the blastocyl cavity, umbilical cord blood, or maternalcirculation which may be of fetal or maternal origin. The biologicalsample may also be a tissue sample or biopsy from which vesicles andother circulating biomarkers may be obtained. For example, cells fromthe sample can be cultured and vesicles isolated from the culture (seefor example, Example 1). In various embodiments, biomarkers or moreparticularly biosignatures disclosed herein can be assessed directlyfrom such biological samples (e.g., identification of presence or levelsof nucleic acid or polypeptide biomarkers or functional fragmentsthereof) utilizing various methods, such as extraction of nucleic acidmolecules from blood, plasma, serum or any of the foregoing biologicalsamples, use of protein or antibody arrays to identify polypeptide (orfunctional fragment) biomarker(s), as well as other array, sequencing,PCR and proteomic techniques known in the art for identification andassessment of nucleic acid and polypeptide molecules.

Table 1 lists illustrative examples of diseases, conditions, orbiological states and a corresponding list of biological samples fromwhich vesicles may be analyzed.

TABLE 1 Examples of Biological Samples for Vesicle Analysis for VariousDiseases, Conditions, or Biological States Illustrative Disease,Condition or Biological State Illustrative Biological SamplesCancers/neoplasms affecting the following tissue Blood, serum, plasma,cerebrospinal fluid (CSF), types/bodily systems: breast, lung, ovarian,colon, urine, sputum, ascites, synovial fluid, semen, nipple rectal,prostate, pancreatic, brain, bone, connective aspirates, saliva,bronchoalveolar lavage fluid, tears, tissue, glands, skin, lymph,nervous system, endocrine, oropharyngeal washes, feces, peritonealfluids, pleural germ cell, genitourinary, hematologic/blood, boneeffusion, sweat, tears, aqueous humor, pericardial marrow, muscle, eye,esophageal, fat tissue, thyroid, fluid, lymph, chyme, chyle, bile, stoolwater, amniotic pituitary, spinal cord, bile duct, heart, gall bladder,fluid, breast milk, pancreatic juice, cerumen, Cowper's bladder, testes,cervical, endometrial, renal, ovarian, fluid or pre-ejaculatory fluid,female ejaculate, digestive/gastrointestinal, stomach, head and neck,interstitial fluid, menses, mucus, pus, sebum, vaginal liver, leukemia,respiratory/thorasic, cancers of lubrication, vomit unknown primary(CUP) Neurodegenerative/neurological disorders: Blood, serum, plasma,CSF, urine Parkinson's disease, Alzheimer's Disease and multiplesclerosis, Schizophrenia, and bipolar disorder, spasticity disorders,epilepsy Cardiovascular Disease: atherosclerosis, Blood, serum, plasma,CSF, urine cardiomyopathy, endocarditis, vunerable plaques, infectionStroke: ischemic, intracerebral hemorrhage, Blood, serum, plasma, CSF,urine subarachnoid hemorrhage, transient ischemic attacks (TIA) Paindisorders: peripheral neuropathic pain and Blood, serum, plasma, CSF,urine chronic neuropathic pain, and fibromyalgia, Autoimmune disease:systemic and localized diseases, Blood, serum, plasma, CSF, urine,synovial fluid rheumatic disease, Lupus, Sjogren's syndrome Digestivesystem abnormalities: Barrett's esophagus, Blood, serum, plasma, CSF,urine irritable bowel syndrome, ulcerative colitis, Crohn's disease,Diverticulosis and Diverticulitis, Celiac Disease Endocrine disorders:diabetes mellitus, various forms Blood, serum, plasma, CSF, urine ofThyroiditis,, adrenal disorders, pituitary disorders Diseases anddisorders of the skin: psoriasis Blood, serum, plasma, CSF, urine,synovial fluid, tears Urological disorders: benign prostatic hypertrophyBlood, serum, plasma, urine (BPH), polycystic kidney disease,interstitial cystitis Hepatic disease/injury: Cirrhosis, induced Blood,serum, plasma, urine hepatotoxicity (due to exposure to natural orsynthetic chemical sources) Kidney disease/injury: acute, sub-acute,chronic Blood, serum, plasma, urine conditions, Podocyte injury, focalsegmental glomerulosclerosis Endometriosis Blood, serum, plasma, urine,vaginal fluids Osteoporosis Blood, serum, plasma, urine, synovial fluidPancreatitis Blood, serum, plasma, urine, pancreatic juice Asthma Blood,serum, plasma, urine, sputum, bronchiolar lavage fluid Allergies Blood,serum, plasma, urine, sputum, bronchiolar lavage fluid Prion-relateddiseases Blood, serum, plasma, CSF, urine Viral Infections: HIV/AIDSBlood, serum, plasma, urine Sepsis Blood, serum, plasma, urine, tears,nasal lavage Organ rejection/transplantation Blood, serum, plasma,urine, various lavage fluids Differentiating conditions: adenoma versusBlood, serum, plasma, urine, sputum, feces, colonic hyperplastic polyp,irritable bowel syndrome (IBS) lavage fluid versus normal, classifyingDukes stages A, B, C, and/or D of colon cancer, adenoma with low-gradehyperplasia versus high-grade hyperplasia, adenoma versus normal,colorectal cancer versus normal, IBS versus. ulcerative colitis (UC)versus Crohn's disease (CD), Pregnancy related physiological states,conditions, or Maternal serum, plasma, amniotic fluid, cord bloodaffiliated diseases: genetic risk, adverse pregnancy outcomes

The methods of the invention can be used to characterize a phenotypeusing a blood sample or blood derivative. Blood derivatives includeplasma and serum. Blood plasma is the liquid component of whole blood,and makes up approximately 55% of the total blood volume. It is composedprimarily of water with small amounts of minerals, salts, ions,nutrients, and proteins in solution. In whole blood, red blood cells,leukocytes, and platelets are suspended within the plasma. Blood serumrefers to blood plasma without fibrinogen or other clotting factors(i.e., whole blood minus both the cells and the clotting factors).

The biological sample may be obtained through a third party, such as aparty not performing the analysis of the biomarkers, whether directassessment of a biological sample or by profiling one or more vesiclesobtained from the biological sample. For example, the sample may beobtained through a clinician, physician, or other health care manager ofa subject from which the sample is derived. Alternatively, thebiological sample may obtained by the same party analyzing the vesicle.In addition, biological samples be assayed, are archived (e.g., frozen)or ortherwise stored in under preservative conditions.

The volume of the biological sample used for biomarker analysis can bein the range of between 0.1-20 mL, such as less than about 20, 15, 10,9, 8, 7, 6, 5, 4, 3, 2, 1 or 0.1 mL.

A sample of bodily fluid can be used as a sample for characterizing aphenotype. For example, biomarkers in the sample can be assessed toprovide a diagnosis, prognosis and/or theranosis of a disease. Thebiomarkers can be circulating biomarkers, such as circulating proteinsor nucleic acids. The biomarkers can also be associated with a vesicleor vesicle population. Methods of the invention can be applied to assessone or more vesicles, as well as one or more different vesiclepopulations that may be present in a biological sample or in a subject.Analysis of one or more biomarkers in a biological sample can be used todetermine whether an additional biological sample should be obtained foranalysis. For example, analysis of one or more vesicles in a sample ofbodily fluid can aid in determining whether a tissue biopsy should beobtained.

A sample from a patient can be collected under conditions that preservethe circulating biomarkers and other entities of interest containedtherein for subsequent analysis. In an embodiment, the samples areprocessed using one or more of CellSave Preservative Tubes (Veridex,North Raritan, N.J.), PAXgene Blood DNA Tubes (QIAGEN GmbH, Germany),and RNAlater (QIAGEN GmbH, Germany).

CellSave Preservative Tubes (CellSave tubes) are sterile evacuated bloodcollection tubes. Each tube contains a solution that contains Na2EDTAand a cell preservative. The EDTA absorbs calcium ions, which can reduceor eliminate blood clotting. The preservative preserves the morphologyand cell surface antigen expression of epithelial and other cells. Thecollection and processing can be performed as described in a protocolprovided by the manufacturer. Each tube is evacuated to withdraw venouswhole blood following standard phlebotomy procedures as known to thoseof skill in the art. CellSave tubes are disclosed in U.S. Pat. Nos.5,466,574; 5,512,332; 5,597,531; 5,698,271; 5,985,153; 5,993,665;6,120,856; 6,136,182; 6,365,362; 6,551,843; 6,620,627; 6,623,982;6,645,731; 6,660,159; 6,790,366; 6,861,259; 6,890,426; 7,011,794;7,282,350; 7,332,288; 5,849,517 and 5,459,073, each of which isincorporated by reference in its entirety herein.

The PAXgene Blood DNA Tube (PAXgene tube) is a plastic, evacuated tubefor the collection of whole blood for the isolation of nucleic acids.The tubes can be used for blood collection, transport and storage ofwhole blood specimens and isolation of nucleic acids contained therein,e.g., DNA or RNA. Blood is collected under a standard phlebotomyprotocol into an evacuated tube that contains an additive. Thecollection and processing can be performed as described in a protocolprovided by the manufacturer. PAXgene tubes are disclosed in U.S. Pat.Nos. 5,906,744; 4,741,446; 4,991,104, each of which is incorporated byreference in its entirety herein.

The RNAlater RNA Stabilization Reagent (RNAlater) is used for immediatestabilization of RNA in tissues. RNA can be unstable in harvestedsamples. The aqueous RNAlater reagent permeates tissues and otherbiological samples, thereby stabilizing and protecting the RNA containedtherein. Such protection helps ensure that downstream analyses reflectthe expression profile of the RNA in the tissue or other sample. Thesamples are submerged in an appropriate volume of RNAlater reagentimmediately after harvesting. The collection and processing can beperformed as described in a protocol provided by the manufacturer.According to the manufacturer, the reagent preserves RNA for up to 1 dayat 37° C., 7 days at 18-25° C., or 4 weeks at 2-8° C., allowingprocessing, transportation, storage, and shipping of samples withoutliquid nitrogen or dry ice. The samples can also be placed at −20° C. or−80° C., e.g., for archival storage. The preserved samples can be usedto analyze any type of RNA, including without limitation total RNA,mRNA, and microRNA. RNAlater can also be useful for collecting samplesfor DNA, RNA and protein analysis. RNAlater is disclosed in U.S. Pat.No. 5,346,994, each of which is incorporated by reference in itsentirety herein.

Vesicles

Methods of the invention can include assessing one or more vesicles,including assessing vesicle populations. A vesicle, as used herein, is amembrane vesicle that is shed from cells. Vesicles or membrane vesiclesinclude without limitation: circulating microvesicles (cMVs),microvesicle, exosome, nanovesicle, dexosome, bleb, blebby, prostasome,microparticle, intralumenal vesicle, membrane fragment, intralumenalendosomal vesicle, endosomal-like vesicle, exocytosis vehicle, endosomevesicle, endosomal vesicle, apoptotic body, multivesicular body,secretory vesicle, phospholipid vesicle, liposomal vesicle, argosome,texasome, secresome, tolerosome, melanosome, oncosome, or exocytosedvehicle. Furthermore, although vesicles may be produced by differentcellular processes, the methods of the invention are not limited to orreliant on any one mechanism, insofar as such vesicles are present in abiological sample and are capable of being characterized by the methodsdisclosed herein. Unless otherwise specified, methods that make use of aspecies of vesicle can be applied to other types of vesicles. Vesiclescomprise spherical structures with a lipid bilayer similar to cellmembranes which surrounds an inner compartment which can contain solublecomponents, sometimes referred to as the payload. In some embodiments,the methods of the invention make use of exosomes, which are smallsecreted vesicles of about 40-100 nm in diameter. For a review ofmembrane vesicles, including types and characterizations, see Thery etal., Nat Rev Immunol. 2009 August; 9(8): 581-93. Some properties ofdifferent types of vesicles include those in Table 2:

TABLE 2 Vesicle Properties Membrane Exosome- Apoptotic Feature ExosomesMicrovesicles Ectosomes particles like vesicles vesicles Size   50-100nm 100-1,000 nm 50-200 nm   50-80 nm 20-50 nm   50-500 nm Density in1.13-1.19 g/ml 1.04-1.07 g/ml 1.1 g/ml 1.16-1.28 g/ml sucrose EM Cupshape Irregular shape, Bilamellar Round Irregular Heterogeneousappearance electron dense round shape structures Sedimentation 100,000 g10,000 g 160,000- 100,000- 175.000 g  1,200 g, 200,000 g 200,000 g 10,000 g, 100,000 g Lipid Enriched in Expose PPS Enriched in No lipidcomposition cholesterol, cholesterol and rafts sphingomyelindiacylglycerol; and ceramide; expose PPS contains lipid rafts; exposePPS Major protein Tetraspanins Integrins, CR1 and CD133; no TNFRIHistones markers (e.g., CD63, selectins and proteolytic CD63 CD9), Alix,CD40 ligand enzymes; no TSG101 CD63 Intracellular Internal Plasma PlasmaPlasma origin compartments membrane membrane membrane (endosomes)Abbreviations: phosphatidylserine (PPS); electron microscopy (EM)

Vesicles include shed membrane bound particles, or “microparticles,”that are derived from either the plasma membrane or an internalmembrane. Vesicles can be released into the extracellular environmentfrom cells. Cells releasing vesicles include without limitation cellsthat originate from, or are derived from, the ectoderm, endoderm, ormesoderm. The cells may have undergone genetic, environmental, and/orany other variations or alterations. For example, the cell can be tumorcells. A vesicle can reflect any changes in the source cell, and therebyreflect changes in the originating cells, e.g., cells having variousgenetic mutations. In one mechanism, a vesicle is generatedintracellularly when a segment of the cell membrane spontaneouslyinvaginates and is ultimately exocytosed (see for example, Keller etal., Immunol. Lett. 107 (2): 102-8 (2006)). Vesicles also includecell-derived structures bounded by a lipid bilayer membrane arising fromboth herniated evagination (blebbing) separation and sealing of portionsof the plasma membrane or from the export of any intracellularmembrane-bounded vesicular structure containing variousmembrane-associated proteins of tumor origin, including surface-boundmolecules derived from the host circulation that bind selectively to thetumor-derived proteins together with molecules contained in the vesiclelumen, including but not limited to tumor-derived microRNAs orintracellular proteins. Blebs and blebbing are further described inCharras et al., Nature Reviews Molecular and Cell Biology, Vol. 9, No.11, p. 730-736 (2008). A vesicle shed into circulation or bodily fluidsfrom tumor cells may be referred to as a “circulating tumor-derivedvesicle.” When such vesicle is an exosome, it may be referred to as acirculating-tumor derived exosome (CTE). In some instances, a vesiclecan be derived from a specific cell of origin. CTE, as with acell-of-origin specific vesicle, typically have one or more uniquebiomarkers that permit isolation of the CTE or cell-of-origin specificvesicle, e.g., from a bodily fluid and sometimes in a specific manner.For example, a cell or tissue specific markers are utilized to identifythe cell of origin. Examples of such cell or tissue specific markers aredisclosed herein and can further be accessed in the Tissue-specific GeneExpression and Regulation (TiGER) Database, available atbioinfo.wilmer.jhu.edu/tiger/; Liu et al. (2008) TiGER: a database fortissue-specific gene expression and regulation. BMC Bioinformatics.9:271; TissueDistributionDBs, available at genomedkfz-heidelberg.de/menu/tissue_db/index.html.

A vesicle can have a diameter of greater than about 10 nm, 20 nm, or 30nm. A vesicle can have a diameter of greater than 40 nm, 50 nm, 100 nm,200 nm, 500 nm, 1000 nm or greater than 10,000 nm. A vesicle can have adiameter of about 30-1000 nm, about 30-800 nm, about 30-200 nm, or about30-100 nm. In some embodiments, the vesicle has a diameter of less than10,000 nm, 1000 nm, 800 nm, 500 nm, 200 nm, 100 nm, 50 nm, 40 nm, 30 nm,20 nm or less than 10 nm. As used herein the term “about” in referenceto a numerical value means that variations of 10% above or below thenumerical value are within the range ascribed to the specified value.Typical sizes for various types of vesicles are shown in Table 2.Vesicles can be assessed to measure the diameter of a single vesicle orany number of vesicles. For example, the range of diameters of a vesiclepopulation or an average diameter of a vesicle population can bedetermined. Vesicle diameter can be assessed using methods known in theart, e.g., imaging technologies such as electron microscopy. In anembodiment, a diameter of one or more vesicles is determined usingoptical particle detection. See, e.g., U.S. Pat. No. 7,751,053, entitled“Optical Detection and Analysis of Particles” and issued Jul. 6, 2010;and U.S. Pat. No. 7,399,600, entitled “Optical Detection and Analysis ofParticles” and issued Jul. 15, 2010.

In some embodiments, vesicles are directly assayed from a biologicalsample without prior isolation, purification, or concentration from thebiological sample. For example, the amount of vesicles in the sample canby itself provide a biosignature that provides a diagnostic, prognosticor theranostic determination. Alternatively, the vesicle in the samplemay be isolated, captured, purified, or concentrated from a sample priorto analysis. As noted, isolation, capture or purification as used hereincomprises partial isolation, partial capture or partial purificationapart from other components in the sample. Vesicle isolation can beperformed using various techniques as described herein, e.g.,chromatography, filtration, centrifugation, flow cytometry, affinitycapture (e.g., to a planar surface or bead), and/or using microfluidics.

Vesicles such as exosomes can be assessed to provide a phenotypiccharacterization by comparing vesicle characteristics to a reference. Insome embodiments, surface antigens on a vesicle are assessed. Thesurface antigens can provide an indication of the anatomical originand/or cellular of the vesicles and other phenotypic information, e.g.,tumor status. For example, wherein vesicles found in a patient sample,e.g., a bodily fluid such as blood, serum or plasma, are assessed forsurface antigens indicative of colorectal origin and the presence ofcancer. The surface antigens may comprise any informative biologicalentity that can be detected on the vesicle membrane surface, includingwithout limitation surface proteins, lipids, carbohydrates, and othermembrane components. For example, positive detection of colon derivedvesicles expressing tumor antigens can indicate that the patient hascolorectal cancer. As such, methods of the invention can be used tocharacterize any disease or condition associated with an anatomical orcellular origin, by assessing, for example, disease-specific andcell-specific biomarkers of one or more vesicles obtained from asubject.

In another embodiment, one or more vesicle payloads are assessed toprovide a phenotypic characterization. The payload with a vesiclecomprises any informative biological entity that can be detected asencapsulated within the vesicle, including without limitation proteinsand nucleic acids, e.g., genomic or cDNA, mRNA, or functional fragmentsthereof, as well as microRNAs (miRs). In addition, methods of theinvention are directed to detecting vesicle surface antigens (inaddition or exclusive to vesicle payload) to provide a phenotypiccharacterization. For example, vesicles can be characterized by usingbinding agents (e.g., antibodies or aptamers) that are specific tovesicle surface antigens, and the bound vesicles can be further assessedto identify one or more payload components disclosed therein. Asdescribed herein, the levels of vesicles with surface antigens ofinterest or with payload of interest can be compared to a reference tocharacterize a phenotype. For example, overexpression in a sample ofcancer-related surface antigens or vesicle payload, e.g., a tumorassociated mRNA or microRNA, as compared to a reference, can indicatethe presence of cancer in the sample. The biomarkers assessed can bepresent or absent, increased or reduced based on the selection of thedesired target sample and comparison of the target sample to the desiredreference sample. Non-limiting examples of target samples include:disease; treated/not-treated; different time points, such as a in alongitudinal study; and non-limiting examples of reference sample:non-disease; normal; different time points; and sensitive or resistantto candidate treatment(s).

MicroRNA

Various biomarker molecules can be assessed in biological samples orvesicles obtained from such biological samples. MicroRNAs comprise oneclass biomarkers assessed via methods of the invention. MicroRNAs, alsoreferred to herein as miRNAs or miRs, are short RNA strandsapproximately 21-23 nucleotides in length. MiRNAs are encoded by genesthat are transcribed from DNA but are not translated into protein andthus comprise non-coding RNA. The miRs are processed from primarytranscripts known as pri-miRNA to short stem-loop structures calledpre-miRNA and finally to the resulting single strand miRNA. Thepre-miRNA typically forms a structure that folds back on itself inself-complementary regions. These structures are then processed by thenuclease Dicer in animals or DCL1 in plants. Mature miRNA molecules arepartially complementary to one or more messenger RNA (mRNA) moleculesand can function to regulate translation of proteins. Identifiedsequences of miRNA can be accessed at publicly available databases, suchas www.microRNA.org, www.mirbase.org, orwww.mirz.unibas.ch/cgi/miRNA.cgi.

miRNAs are generally assigned a number according to the namingconvention “mir-[number].” The number of a miRNA is assigned accordingto its order of discovery relative to previously identified miRNAspecies. For example, if the last published miRNA was mir-121, the nextdiscovered miRNA will be named mir-122, etc. When a miRNA is discoveredthat is homologous to a known miRNA from a different organism, the namecan be given an optional organism identifier, of the form [organismidentifier]-mir-[number]. Identifiers include hsa for Homo sapiens andmmu for Mus Musculus. For example, a human homolog to mir-121 might bereferred to as hsa-mir-121 whereas the mouse homolog can be referred toas mmu-mir-121.

Mature microRNA is commonly designated with the prefix “miR” whereas thegene or precursor miRNA is designated with the prefix “mir.” Forexample, mir-121 is a precursor for miR-121. When differing miRNA genesor precursors are processed into identical mature miRNAs, thegenes/precursors can be delineated by a numbered suffix. For example,mir-121-1 and mir-121-2 can refer to distinct genes or precursors thatare processed into miR-121. Lettered suffixes are used to indicateclosely related mature sequences. For example, mir-121a and mir-121b canbe processed to closely related miRNAs miR-121a and miR-121b,respectively. In the context of the invention, any microRNA (miRNA ormiR) designated herein with the prefix mir-* or miR-* is understood toencompass both the precursor and/or mature species, unless otherwiseexplicitly stated otherwise.

Sometimes it is observed that two mature miRNA sequences originate fromthe same precursor. When one of the sequences is more abundant that theother, a “*” suffix can be used to designate the less common variant.For example, miR-121 would be the predominant product whereas miR-121*is the less common variant found on the opposite arm of the precursor.If the predominant variant is not identified, the miRs can bedistinguished by the suffix “5p” for the variant from the 5′ arm of theprecursor and the suffix “3p” for the variant from the 3′ arm. Forexample, miR-121-5p originates from the 5′ arm of the precursor whereasmiR-121-3p originates from the 3′ arm. Less commonly, the 5p and 3pvariants are referred to as the sense (“s”) and anti-sense (“as”) forms,respectively. For example, miR-121-5p may be referred to as miR-121-swhereas miR-121-3p may be referred to as miR-121-as.

The above naming conventions have evolved over time and are generalguidelines rather than absolute rules. For example, the let− and lin−families of miRNAs continue to be referred to by these monikers. Themir/miR convention for precursor/mature forms is also a guideline andcontext should be taken into account to determine which form is referredto. Further details of miR naming can be found at www.mirbase.org orAmbros et al., A uniform system for microRNA annotation, RNA 9:277-279(2003).

Plant miRNAs follow a different naming convention as described in Meyerset al., Plant Cell. 2008 20(12):3186-3190.

A number of miRNAs are involved in gene regulation, and miRNAs are partof a growing class of non-coding RNAs that is now recognized as a majortier of gene control. In some cases, miRNAs can interrupt translation bybinding to regulatory sites embedded in the 3′-UTRs of their targetmRNAs, leading to the repression of translation. Target recognitioninvolves complementary base pairing of the target site with the miRNA'sseed region (positions 2-8 at the miRNA's 5′ end), although the exactextent of seed complementarity is not precisely determined and can bemodified by 3′ pairing. In other cases, miRNAs function like smallinterfering RNAs (siRNA) and bind to perfectly complementary mRNAsequences to destroy the target transcript.

Characterization of a number of miRNAs indicates that they influence avariety of processes, including early development, cell proliferationand cell death, apoptosis and fat metabolism. For example, some miRNAs,such as lin-4, let-7, mir-14, mir-23, and bantam, have been shown toplay critical roles in cell differentiation and tissue development.Others are believed to have similarly important roles because of theirdifferential spatial and temporal expression patterns.

The miRNA database available at miRBase (www.mirbase.org) comprises asearchable database of published miRNA sequences and annotation. Furtherinformation about miRBase can be found in the following articles, eachof which is incorporated by reference in its entirety herein:Griffiths-Jones et al., miRBase: tools for microRNA genomics. NAR 200836(Database Issue):D154-D158; Griffiths-Jones et al., miRBase: microRNAsequences, targets and gene nomenclature. NAR 2006 34(DatabaseIssue):D140-D144; and Griffiths-Jones, S. The microRNA Registry. NAR2004 32(Database Issue):D109-D111. Representative miRNAs contained inRelease 16 of miRBase, made available September 2010.

As described herein, microRNAs are known to be involved in cancer andother diseases and can be assessed in order to characterize a phenotypein a sample. See, e.g., Ferracin et al., Micromarkers: miRNAs in cancerdiagnosis and prognosis, Exp Rev Mol Diag, April 2010, Vol. 10, No. 3,Pages 297-308; Fabbri, miRNAs as molecular biomarkers of cancer, Exp RevMol Diag, May 2010, Vol. 10, No. 4, Pages 435-444. Techniques to isolateand characterize vesicles and miRs are known to those of skill in theart. In addition to the methodology presented herein, additional methodscan be found in U.S. Pat. No. 7,888,035, entitled “METHODS FOR ASSESSINGRNA PATTERNS” and issued Feb. 15, 2011; and International PatentApplication Nos. PCT/US2010/058461, entitled “METHODS AND SYSTEMS FORISOLATING, STORING, AND ANALYZING VESICLES” and filed Nov. 30, 2010; andPCT/US2011/021160, entitled “DETECTION OF GASTROINTESTINAL DISORDERS”and filed Jan. 13, 2011; each of which applications are incorporated byreference herein in their entirety.

Circulating Biomarkers

Circulating biomarkers include biomarkers that are detectable in bodyfluids, such as blood, plasma, serum. Examples of circulating cancerbiomarkers include cardiac troponin T (cTnT), prostate specific antigen(PSA) for prostate cancer and CA125 for ovarian cancer. Circulatingbiomarkers according to the invention include any appropriate biomarkerthat can be detected in bodily fluid, including without limitationprotein, nucleic acids, e.g., DNA, mRNA and microRNA, lipids,carbohydrates and metabolites. Circulating biomarkers can includebiomarkers that are not associated with cells, such as biomarkers thatare membrane associated, embedded in membrane fragments, part of abiological complex, or free in solution. In one embodiment, circulatingbiomarkers are biomarkers that are associated with one or more vesiclespresent in the biological fluid of a subject.

Circulating biomarkers have been identified for use in characterizationof various phenotypes. See, e.g., Ahmed N, et al., Proteomic-basedidentification of haptoglobin-1 precursor as a novel circulatingbiomarker of ovarian cancer. Br. J. Cancer 2004; Mathelin et al.,Circulating proteinic biomarkers and breast cancer, Gynecol ObstetFertil. 2006 July-August; 34(7-8):638-46. Epub 2006 Jul. 28; Ye et al.,Recent technical strategies to identify diagnostic biomarkers forovarian cancer. Expert Rev Proteomics. 2007 February; 4(1):121-31;Carney, Circulating oncoproteins HER2/neu, EGFR and CAIX (MN) as novelcancer biomarkers. Expert Rev Mol Diagn. 2007 May; 7(3):309-19; Gagnon,Discovery and application of protein biomarkers for ovarian cancer, CurrOpin Obstet. Gynecol. 2008 February; 20(1):9-13; Pasterkamp et al.,Immune regulatory cells: circulating biomarker factories incardiovascular disease. Clin Sci (Load). 2008 August; 115(4):129-31;Fabbri, miRNAs as molecular biomarkers of cancer, Exp Rev Mol Diag, May2010, Vol. 10, No. 4, Pages 435-444; PCT Patent PublicationWO/2007/088537; U.S. Pat. Nos. 7,745,150 and 7,655,479; U.S. PatentPublications 20110008808, 20100330683, 20100248290, 20100222230,20100203566, 20100173788, 20090291932, 20090239246, 20090226937,20090111121, 20090004687, 20080261258, 20080213907, 20060003465,20050124071, and 20040096915, each of which publication is incorporatedherein by reference in its entirety.

Vesicle Enrichment

A vesicle or a population of vesicles may be isolated, purified,concentrated or otherwise enriched prior to and/or during analysis.Unless otherwise specified, the terms “purified,” “isolated,” “ ” asused herein in reference to vesicles or biomarker components includepartial or complete purification or isolation of such components from acell or organism. Analysis of a vesicle can include quantitiating theamount one or more vesicle populations of a biological sample. Forexample, a heterogeneous population of vesicles can be quantitated, or ahomogeneous population of vesicles, such as a population of vesicleswith a particular biomarker profile, a particular biosignature, orderived from a particular cell type can be isolated from a heterogeneouspopulation of vesicles and quantitated. Analysis of a vesicle can alsoinclude detecting, quantitatively or qualitatively, one or moreparticular biomarker profile or biosignature of a vesicle, as describedherein.

A vesicle can be stored and archived, such as in a bio-fluid bank andretrieved for analysis as necessary. A vesicle may also be isolated froma biological sample that has been previously harvested and stored from aliving or deceased subject. In addition, a vesicle may be isolated froma biological sample which has been collected as described in King etal., Breast Cancer Res 7(5): 198-204 (2005). A vesicle can be isolatedfrom an archived or stored sample. Alternatively, a vesicle may beisolated from a biological sample and analyzed without storing orarchiving of the sample. Furthermore, a third party may obtain or storethe biological sample, or obtain or store the vesicle for analysis.

An enriched population of vesicles can be obtained from a biologicalsample. For example, vesicles may be concentrated or isolated from abiological sample using size exclusion chromatography, density gradientcentrifugation, differential centrifugation, nanomembraneultrafiltration, immunoabsorbent capture, affinity purification,microfluidic separation, or combinations thereof.

Size exclusion chromatography, such as gel permeation columns,centrifugation or density gradient centrifugation, and filtrationmethods can be used. For example, a vesicle can be isolated bydifferential centrifugation, anion exchange and/or gel permeationchromatography (for example, as described in U.S. Pat. Nos. 6,899,863and 6,812,023), sucrose density gradients, organelle electrophoresis(for example, as described in U.S. Pat. No. 7,198,923), magneticactivated cell sorting (MACS), or with a nanomembrane ultrafiltrationconcentrator. Various combinations of isolation or concentration methodscan be used.

Highly abundant proteins, such as albumin and immunoglobulin, may hinderisolation of vesicles from a biological sample. For example, a vesiclecan be isolated from a biological sample using a system that utilizesmultiple antibodies that are specific to the most abundant proteinsfound in a biological sample, such as blood. Such a system can remove upto several proteins at once, thus unveiling the lower abundance speciessuch as cell-of-origin specific vesicles.

This type of system can be used for isolation of vesicles frombiological samples such as blood, cerebrospinal fluid or urine. Theisolation of vesicles from a biological sample may also be enhanced byhigh abundant protein removal methods as described in Chromy et al. JProteome Res 2004; 3:1120-1127. In another embodiment, the isolation ofvesicles from a biological sample may also be enhanced by removing serumproteins using glycopeptide capture as described in Zhang et al, MolCell Proteomics 2005; 4:144-155. In addition, vesicles from a biologicalsample such as urine may be isolated by differential centrifugationfollowed by contact with antibodies directed to cytoplasmic oranti-cytoplasmic epitopes as described in Pisitkun et al., Proc NatlAcad Sci USA, 2004; 101:13368-13373.

Isolation or enrichment of a vesicle from a biological sample can alsobe enhanced by use of sonication (for example, by applying ultrasound),detergents, other membrane-activating agents, or any combinationthereof. For example, ultrasonic energy can be applied to a potentialtumor site, and without being bound by theory, release of vesicles froma tissue can be increased, allowing an enriched population of vesiclesthat can be analyzed or assessed from a biological sample using one ormore methods disclosed herein.

Sample Handling

With methods of detecting circulating biomarkers as described here,e.g., antibody affinity isolation, the consistency of the results can beoptimized as necessary using various concentration or isolationprocedures. Such steps can include agitation such as shaking orvortexing, different isolation techniques such as polymer basedisolation, e.g., with PEG, and concentration to different levels duringfiltration or other steps. It will be understood by those in the artthat such treatments can be applied at various stages of testing thevesicle containing sample. In one embodiment, the sample itself, e.g., abodily fluid such as plasma or serum, is vortexed. In some embodiments,the sample is vortexed after one or more sample treatment step, e.g.,vesicle isolation, has occurred. Agitation can occur at some or allappropriate sample treatment steps as desired. Additives can beintroduced at the various steps to improve the process, e.g., to controlaggregation or degradation of the biomarkers of interest.

The results can also be optimized as desirable by treating the samplewith various agents. Such agents include additives to controlaggregation and/or additives to adjust pH or ionic strength. Additivesthat control aggregation include blocking agents such as bovine serumalbumin (BSA) and milk, chaotropic agents such as guanidium hydrochloride, and detergents or surfactants. Useful ionic detergents includesodium dodecyl sulfate (SDS, sodium lauryl sulfate (SLS)), sodiumlaureth sulfate (SLS, sodium lauryl ether sulfate (SLES)), ammoniumlauryl sulfate (ALS), cetrimonium bromide, cetrimonium chloride,cetrimonium stearate, and the like. Useful non-ionic (zwitterionic)detergents include polyoxyethylene glycols, polysorbate 20 (also knownas Tween 20), other polysorbates (e.g., 40, 60, 65, 80, etc), Triton-X(e.g., X100, X114),3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate (CHAPS),CHAPSO, deoxycholic acid, sodium deoxycholate, NP-40, glycosides,octyl-thio-glucosides, maltosides, and the like. In some embodiments,Pluronic F-68, a surfactant shown to reduce platelet aggregation, isused to treat samples containing vesicles during isolation and/ordetection. F68 can be used from a 0.1% to 10% concentration, e.g., a 1%,2.5% or 5% concentration. The pH and/or ionic strength of the solutioncan be adjusted with various acids, bases, buffers or salts, includingwithout limitation sodium chloride (NaCl), phosphate-buffered saline(PBS), tris-buffered saline (TBS), sodium phosphate, potassium chloride,potassium phosphate, sodium citrate and saline-sodium citrate (SSC)buffer. In some embodiments, NaCl is added at a concentration of 0.1% to10%, e.g., 1%, 2.5% or 5% final concentration. In some embodiments,Tween 20 is added to 0.005 to 2% concentration, e.g., 0.05%, 0.25% or0.5% final concentration. Blocking agents for use with the inventioncomprise inert proteins, e.g., milk proteins, non-fat dry milk protein,albumin, BSA, casein, or serum such as newborn calf serum (NBCS), goatserum, rabbit serum or salmon serum. The proteins can be added at a 0.1%to 10% concentration, e.g., 1%, 2%, 3%, 3.5%, 4%, 5%, 6%, 7%, 8%, 9% or10% concentration. In some embodiments, BSA is added to 0.1% to 10%concentration, e.g., 1%, 2%, 3%, 3.5%, 4%, 5%, 6%, 7%, 8%, 9% or 10%concentration. In an embodiment, the sample is treated according to themethodology presented in U.S. patent application Ser. No. 11/632,946,filed Jul. 13, 2005, which application is incorporated herein byreference in its entirety. Commercially available blockers may be used,such as SuperBlock, StartingBlock, Protein-Free from Pierce (a divisionof Thermo Fisher Scientific, Rockford, Ill.). In some embodiments,SSC/detergent (e.g., 20×SSC with 0.5% Tween 20 or 0.1% Triton-X 100) isadded to 0.1% to 10% concentration, e.g., at 1.0% or 5.0% concentration.

The methods of detecting vesicles and other circulating biomarkers canbe optimized as desired with various combinations of protocols andtreatments as described herein. A detection protocol can be optimized byvarious combinations of agitation, isolation methods, and additives. Insome embodiments, the patient sample is vortexed before and afterisolation steps, and the sample is treated with blocking agentsincluding BSA and/or F68. Such treatments may reduce the formation oflarge aggregates or protein or other biological debris and thus providea more consistent detection reading.

Filters

A vesicle can be isolated from a biological sample by filtering abiological sample from a subject through a filtration module andcollecting from the filtration module a retentate comprising thevesicle, thereby isolating the vesicle from the biological sample. Themethod can comprise filtering a biological sample from a subject througha filtration module comprising a filter; and collecting from thefiltration module a retentate comprising the vesicle, thereby isolatingthe vesicle from the biological sample. In one embodiment, the filterretains molecules greater than about 100 kiloDaltons.

The method can further comprise determining a biosignature of thevesicle. The method can also further comprise applying the retentate toa plurality of substrates, wherein each substrate is coupled to one ormore capture agents, and each subset of the plurality of substratescomprises a different capture agent or combination of capture agentsthan another subset of the plurality of substrates.

Also provided herein is a method of determining a biosignature of avesicle in a sample comprising: filtering a biological sample from asubject with a disorder through a filtration module, collecting from thefiltration module a retentate comprising one or more vesicles, anddetermining a biosignature of the one or more vesicles. In oneembodiment, the filtration module comprises a filter that retainsmolecules greater than about 100 or 150 kiloDaltons.

The method disclosed herein can further comprise characterizing aphenotype in a subject by filtering a biological sample from a subjectthrough a filtration module, collecting from the filtration module aretentate comprising one or more vesicles; detecting a biosignature ofthe one or more vesicles; and characterizing a phenotype in the subjectbased on the biosignature, wherein characterizing is with at least 70%sensitivity. In some embodiments, characterizing comprises determiningan amount of one or more vesicle having the biosignature. Furthermore,the characterizing can be from about 80% to 100% sensitivity.

Also provided herein is a method for multiplex analysis of a pluralityof vesicles. In some embodiments, the method comprises filtering abiological sample from a subject through a filtration module; collectingfrom the filtration module a retentate comprising the plurality ofvesicles, applying the plurality of vesicles to a plurality of captureagents, wherein the plurality of capture agents is coupled to aplurality of substrates, and each subset of the plurality of substratesis differentially labeled from another subset of the plurality ofsubstrates; capturing at least a subset of the plurality of vesicles;and determining a biosignature for at least a subset of the capturedvesicles. In one embodiment, each substrate is coupled to one or morecapture agents, and each subset of the plurality of substrates comprisesa different capture agent or combination of capture agents as comparedto another subset of the plurality of substrates. In some embodiments,at least a subset of the plurality of substrates is intrinsicallylabeled, such as comprising one or more labels. The substrate can be aparticle or bead, or any combination thereof. In one embodiment, thefiltration module comprises a filter that retains molecules greater thanabout 100 or 150 kiloDaltons.

In some embodiments, the method for multiplex analysis of a plurality ofvesicles comprises filtering a biological sample from a subject througha filtration module, wherein the filtration module comprises a filterthat retains molecules greater than about 100 kiloDaltons; collectingfrom the filtration module a retentate comprising the plurality ofvesicles; applying the plurality of vesicles to a plurality of captureagents, wherein the plurality of capture agents is coupled to amicroarray; capturing at least a subset of the plurality of vesicles onthe microarray; and determining a biosignature for at least a subset ofthe captured vesicles. In one embodiment, the filtration modulecomprises a filter that retains molecules greater than about 100 or 150kiloDaltons.

The biological sample can be clarified prior to isolation by filtration.For example, non-vesicle components such as cellular debris can beremoved. The clarification can be by low-speed centrifugation, such asat about 5,000×g, 4,000×g, 3,000×g, 2,000×g, 1,000×g, or less. Thesupernatant, or clarified biological sample, containing the vesicle canthen be collected and filtered to isolate the vesicle from the clarifiedbiological sample. In some embodiments, the biological sample is notclarified prior to isolation of a vesicle by filtration.

In some embodiments, isolation of a vesicle from a sample does not usehigh-speed centrifugation, such as ultracentrifugation. For example,isolation may not require the use of centrifugal speeds, such as about100,000×g or more. In some embodiments, isolation of a vesicle from asample uses speeds of less than 50,000×g, 40,000×g, 30,000×g, 20,000×g,15,000×g, 12,000×g, or 10,000×g.

The filtration module utilized to isolate the vesicle from thebiological sample can be a fiber-based filtration cartridge. Forexample, the fiber can be a hollow polymeric fiber, such as apolypropylene hollow fiber. A biological sample can be introduced intothe filtration module by pumping the sample fluid, such as a biologicalfluid as disclosed herein, into the module with a pump device, such as aperistaltic pump. The pump flow rate can vary, such as at about 0.25,0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, or 10 mL/minute.

The filtration module can be a membrane filtration module. For example,the membrane filtration module can comprise a filter disc membrane, suchas a hydrophilic polyvinylidene difluoride (PVDF) filter disc membranehoused in a stirred cell apparatus (e.g., comprising a magneticstirrer). In some embodiments, the sample moves through the filter as aresult of a pressure gradient established on either side of the filtermembrane.

The filter can comprise a material having low hydrophobic absorptivityand/or high hydrophilic properties. For example, the filter can have anaverage pore size for vesicle retention and permeation of most proteinsas well as a surface that is hydrophilic, thereby limiting proteinadsorption. For example, the filter can comprise a material selectedfrom the group consisting of polypropylene, PVDF, polyethylene,polyfluoroethylene, cellulose, secondary cellulose acetate,polyvinylalcohol, and ethylenevinyl alcohol (EVAL®, Kuraray Co.,Okayama, Japan). Additional materials that can be used in a filterinclude, but are not limited to, polysulfone and polyethersulfone.

The filtration module can have a filter that retains molecules greaterthan about 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170,180, 190, 200, 250, 300, 400, or 500 kiloDaltons (kDa), such as a filterthat has a MWCO (molecular weight cut off) of about 50, 60, 70, 80, 90,100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 250, 300, 400, or500. In some embodiments, the filter within the filtration module has anaverage pore diameter of about 0.01 μm to about 0.15 μm, and in someembodiments from about 0.05 μm to about 0.12 μm. In some embodiments,the filter has an average pore diameter of about 0.06 μm, 0.07. μm, 0.08μm, 0.09 μm, 0.1 μm, or 0.11 μm.

The filtration module can be a commerically available column, such as acolumn typically used for concentrating proteins or for isoatlingproteins. Examples include, but are not limited to, columns fromMillpore (Billerica, Mass.), such as Amicon® centrifugal filters, orfrom Pierce® (Rockford, Ill.), such as Pierce Concentrator filterdevices. Useful columns from Pierce include disposable ultrafiltrationcentrifugal devices with a MWCO of 9 kDa, 20 kDa and/or 150 kDa. Theseconcentrators consist of a high-performance regenerated cellulosemembrane welded to a conical device. The filters can be as described inU.S. Pat. No. 6,269,957 or 6,357,601, both of which applications areincorporated by reference in their entirety herein.

The retentate comprising the isolated vesicle can be collected from thefiltration module. The retentate can be collected by flushing theretentate from the filter. Selection of a filter composition havinghydrophilic surface properties, thereby limiting protein adsorption, canbe used, without being bound by theory, for easier collection of theretentate and minimize use of harsh or time-consuming collectiontechniques.

The collected retentate can then be used subsequent analysis, such asassessing a biosignature of one or more vesicles in the retentate, asfurther described herein. The analysis can be directly performed on thecollected retentate. Alternatively, the collected retentate can befurther concentrated or purified, prior to analysis of one or morevesicles. For example, the retentate can be further concentrated orvesicles further isolated from the retentate using size exclusionchromatography, density gradient centrifugation, differentialcentrifugation, immunoabsorbent capture, affinity purification,microfluidic separation, or combinations thereof, such as describedherein. In some embodiments, the retentate can undergo another step offiltration. Alternatively, prior to isolation of a vesicle using afilter, the vesicle is concentrated or isolated using size exclusionchromatography, density gradient centrifugation, differentialcentrifugation, immunoabsorbent capture, affinity purification,microfluidic separation, or combinations thereof.

For example, prior to filtering a biological sample through a filtrationmodule with a filter that retains molecules greater than about 50, 60,70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 250,300, 400, or 500 kiloDaltons (kDa), such as a filter that has a MWCO(molecular weight cut off) of about 50, 60, 70, 80, 90, 100, 110, 120,130, 140, 150, 160, 170, 180, 190, 200, 250, 300, 400, or 500, thebiological sample may first be filtered through a filter having aporosity or pore size of between about 0.01 μm to about 2 μm, about 0.05μm to about 1.5 μm, In some embodiments, the filter has a pore size ofabout 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7,1.8, 1.9 or 2.0 μm. The filter may be a syringe filter. Thus, in oneembodiment, the method comprises filtering the biological sample througha filter, such as a syringe filter, wherein the syringe filter has aporosity of greater than about 1 μm, prior to filtering the samplethrough a filtration module comprising a filter that retains moleculesgreater than about 100 or 150 kiloDaltons. In an embodiment, the filteris 1.2 μM filter and the filtration is followed by passage of the samplethrough a 7 ml or 20 ml concentrator column with a 150 kDa cutoff.

The filtration module can be a component of a microfluidic device.Microfluidic devices, which may also be referred to as “lab-on-a-chip”systems, biomedical micro-electro-mechanical systems (bioMEMs), ormulticomponent integrated systems, can be used for isolating, andanalyzing, vesicles. Such systems miniaturize and compartmentalizeprocesses that allow for binding of vesicles, detection of biomarkers,and other processes, such as further described herein

A microfluidic device can also be used for isolation of a vesicle bycomprising a filtration module. For example, a microfluidic device canuse one more channels for isolating a vesicle from a biological samplebased on size from a biological sample. A biological sample can beintroduced into one or more microfluidic channels, which selectivelyallows the passage of vesicles. The microfluidic device can furthercomprise binding agents, or more than one filtration module to selectvesicles based on a property of the vesicles, for example, size, shape,deformability, biomarker profile, or biosignature.

Binding Agents

A binding agent is an agent that binds to a circulating biomarker, suchas a vesicle or a component of a vesicle. The binding agent can be usedas a capture agent and/or a detection agent. A capture agent can bindand capture a circulating biomarker, such as by binding a component orbiomarker of a vesicle. For example, the capture agent can be a captureantibody or capture antigen that binds to an antigen on a vesicle. Adetection agent can bind to a circulating biomarker thereby facilitatingdetection of the biomarker. For example, a capture agent comprising anantigen or aptamer that is sequestered to a substrate can be used tocapture a vesicle in a sample, and a detection agent comprising anantigen or aptamer that carries a label can be used to detect thecaptured vesicle via detection of the detection agent's label. In someembodiments, a vesicle is assessed using capture and detection agentsthat recognize the same vesicle biomarkers. For example, a vesiclepopulation can be captured using a tetraspanin such as by using ananti-CD9 antibody bound to a substrate, and the captured vesicles can bedetected using a fluorescently labeled anti-CD9 antibody to label thecaptured vesicles. In other embodiments, a vesicle is assessed usingcapture and detection agents that recognize different vesiclebiomarkers. For example, a vesicle population can be captured using acell-specific marker such as by using an anti-PCSA antibody bound to asubstrate, and the captured vesicles can be detected using afluorescently labeled anti-CD9 antibody to label the captured vesiclesSimilarly, the vesicle population can be captured using a generalvesicle marker such as by using an anti-CD9 antibody bound to asubstate, and the captured vesicles can be detected using afluorescently labeled antibody to a cell-specific or disease specificmarker to label the captured vesicles.

In one embodiment, a vesicle is captured using a capture agent thatbinds to a biomarker on a vesicle. The capture agent can be coupled to asubstrate and used to isolate a vesicle, as further described herein. Inone embodiment, a capture agent is used for affinity capture orisolation of a vesicle present in a substance or sample.

A binding agent can be used after a vesicle is concentrated or isolatedfrom a biological sample. For example, a vesicle can first be isolatedfrom a biological sample before a vesicle with a specific biosignatureis isolated or detected. The vesicle with a specific biosignature can beisolated or detected using a binding agent for the biomarker. A vesiclewith the specific biomarker can be isolated or detected from aheterogeneous population of vesicles. Alternatively, a binding agent maybe used on a biological sample comprising vesicles without a priorisolation or concentration step. For example, a binding agent is used toisolate or detect a vesicle with a specific biosignature directly from abiological sample.

A binding agent can be a nucleic acid, protein, or other molecule thatcan bind to a component of a vesicle. The binding agent can compriseDNA, RNA, monoclonal antibodies, polyclonal antibodies, Fabs, Fab′,single chain antibodies, synthetic antibodies, aptamers (DNA/RNA),peptoids, zDNA, peptide nucleic acids (PNAs), locked nucleic acids(LNAs), lectins, synthetic or naturally occurring chemical compounds(including but not limited to drugs, labeling reagents), dendrimers, ora combination thereof. For example, the binding agent can be a captureantibody. In embodiments of the invention, the binding agent comprises amembrane protein labeling agent. See, e.g., the membrane proteinlabeling agents disclosed in Alroy et al., US. Patent Publication US2005/0158708. In an embodiment, vesicles are isolated or captured asdescribed herein, and one or more membrane protein labeling agent isused to detect the vesicles.

In some instances, a single binding agent can be employed to isolate ordetect a vesicle. In other instances, a combination of different bindingagents may be employed to isolate or detect a vesicle. For example, atleast 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 25, 50, 75 or 100 different binding agents may be used to isolate ordetect a vesicle from a biological sample. Furthermore, the one or moredifferent binding agents for a vesicle can form a biosignature of avesicle, as further described below.

Different binding agents can also be used for multiplexing. For example,isolation or detection of more than one population of vesicles can beperformed by isolating or detecting each vesicle population with adifferent binding agent. Different binding agents can be bound todifferent particles, wherein the different particles are labeled. Inanother embodiment, an array comprising different binding agents can beused for multiplex analysis, wherein the different binding agents aredifferentially labeled or can be ascertained based on the location ofthe binding agent on the array. Multiplexing can be accomplished up tothe resolution capability of the labels or detection method, such asdescribed below. The binding agents can be used to detect the vesicles,such as for detecting cell-of-origin specific vesicles. A binding agentor multiple binding agents can themselves form a binding agent profilethat provides a biosignature for a vesicle. One or more binding agentscan be selected from FIG. 2. For example, if a vesicle population isdetected or isolated using two, three, four or more binding agents in adifferential detection or isolation of a vesicle from a heterogeneouspopulation of vesicles, the particular binding agent profile for thevesicle population provides a biosignature for the particular vesiclepopulation. The vesicle can be detected using any number of bindingagents in a multiplex fashion. Thus, the binding agent can also be usedto form a biosignature for a vesicle. The biosignature can be used tocharacterize a phenotype.

The binding agent can be a lectin. Lectins are proteins that bindselectively to polysaccharides and glycoproteins and are widelydistributed in plants and animals. For example, lectins such as thosederived from Galanthus nivalis in the form of Galanthus nivalisagglutinin (“GNA”), Narcissus pseudonarcissus in the form of Narcissuspseudonarcissus agglutinin (“NPA”) and the blue green algae Nostocellipsosporum called “cyanovirin” (Boyd et al. Antimicrob AgentsChemother 41(7): 15211530, 1997; Hammar et al. Ann N Y Acad Sci 724: 166169, 1994; Kaku et al. Arch Biochem Biophys 279(2): 298 304, 1990) canbe used to isolate a vesicle. These lectins can bind to glycoproteinshaving a high mannose content (Chervenak et al. Biochemistry 34(16):5685 5695, 1995). High mannose glycoprotein refers to glycoproteinshaving mannose-mannose linkages in the form of α-1→3 or α-1→6mannose-mannose linkages.

The binding agent can be an agent that binds one or more lectins. Lectincapture can be applied to the isolation of the biomarker cathepsin Dsince it is a glycosylated protein capable of binding the lectinsGalanthus nivalis agglutinin (GNA) and concanavalin A (ConA).

Methods and devices for using lectins to capture vesicles are describedin International Patent Applications PCT/US2010/058461, entitled“METHODS AND SYSTEMS FOR ISOLATING, STORING, AND ANALYZING VESICLES” andfiled Nov. 30, 2010; PCT/US2009/066626, entitled “AFFINITY CAPTURE OFCIRCULATING BIOMARKERS” and filed Dec. 3, 2009; PCT/US2010/037467,entitled “METHODS AND MATERIALS FOR ISOLATING EXOSOMES” and filed Jun.4, 2010; and PCT/US2007/006101, entitled “EXTRACORPOREAL REMOVAL OFMICROVESICULAR PARTICLES” and filed Mar. 9, 2007, each of whichapplications is incorporated by reference herein in its entirety.

The binding agent can be an antibody. For example, a vesicle may beisolated using one or more antibodies specific for one or more antigenspresent on the vesicle. For example, a vesicle can have CD63 on itssurface, and an antibody, or capture antibody, for CD63 can be used toisolate the vesicle. Alternatively, a vesicle derived from a tumor cellcan express EpCam, the vesicle can be isolated using an antibody forEpCam and CD63. Other antibodies for isolating vesicles can include anantibody, or capture antibody, to CD9, PSCA, TNFR, CD63, B7H3, MFG-E8,EpCam, Rab, CD81, STEAP, PCSA, PSMA, or 5T4. Other antibodies forisolating vesicles can include an antibody, or capture antibody, to DR3,STEAP, epha2, TMEM211, MFG-E8, Tissue Factor (TF), unc93A, A33, CD24,NGAL, EpCam, MUC17, TROP2, or TETS.

In some embodiments, the capture agent is an antibody to CD9, CD63,CD81, PSMA, PCSA, B7H3, EpCam, PSCA, ICAM, STEAP, or EGFR. The captureagent can also be used to identify a biomarker of a vesicle. Forexample, a capture agent such as an antibody to CD9 would identify CD9as a biomarker of the vesicle. In some embodiments, a plurality ofcapture agents can be used, such as in multiplex analysis. The pluralityof captures agents can comprise binding agents to one or more of: CD9,CD63, CD81, PSMA, PCSA, B7H3, EpCam, PSCA, ICAM, STEAP, and EGFR. Insome embodiments, the plurality of capture agents comprise bindingagents to CD9, CD63, CD81, PSMA, PCSA, B7H3, MFG-E8, and/or EpCam. Inyet other embodiments, the plurality of capture agents comprises bindingagents to CD9, CD63, CD81, PSMA, PCSA, B7H3, EpCam, PSCA, ICAM, STEAP,and/or EGFR. The plurality of capture agents comprises binding agents toTMEM211, MFG-E8, Tissue Factor (TF), and/or CD24.

The antibodies referenced herein can be immunoglobulin molecules orimmunologically active portions of immunoglobulin molecules, i.e.,molecules that contain an antigen binding site that specifically bindsan antigen and synthetic antibodies. The immunoglobulin molecules can beof any class (e.g., IgG, IgE, IgM, IgD or IgA) or subclass ofimmunoglobulin molecule. Antibodies include, but are not limited to,polyclonal, monoclonal, bispecific, synthetic, humanized and chimericantibodies, single chain antibodies, Fab fragments and F(ab′)₂fragments, Fv or Fv′ portions, fragments produced by a Fab expressionlibrary, anti-idiotypic (anti-Id) antibodies, or epitope-bindingfragments of any of the above. An antibody, or generally any molecule,“binds specifically” to an antigen (or other molecule) if the antibodybinds preferentially to the antigen, and, e.g., has less than about 30%,20%, 10%, 5% or 1% cross-reactivity with another molecule.

The binding agent can also be a polypeptide or peptide. Polypeptide isused in its broadest sense and may include a sequence of subunit aminoacids, amino acid analogs, or peptidomimetics. The subunits may belinked by peptide bonds. The polypeptides may be naturally occurring,processed forms of naturally occurring polypeptides (such as byenzymatic digestion), chemically synthesized or recombinantly expressed.The polypeptides for use in the methods of the present invention may bechemically synthesized using standard techniques. The polypeptides maycomprise D-amino acids (which are resistant to L-amino acid-specificproteases), a combination of D- and L-amino acids, β amino acids, orvarious other designer or non-naturally occurring amino acids (e.g.,β-methyl amino acids, Cα-methyl amino acids, and Nα-methyl amino acids,etc.) to convey special properties. Synthetic amino acids may includeornithine for lysine, and norleucine for leucine or isoleucine. Inaddition, the polypeptides can have peptidomimetic bonds, such as esterbonds, to prepare polypeptides with novel properties. For example, apolypeptide may be generated that incorporates a reduced peptide bond,i.e., R₁—CH₂—NH—R₂, where R₁ and R₂ are amino acid residues orsequences. A reduced peptide bond may be introduced as a dipeptidesubunit. Such a polypeptide would be resistant to protease activity, andwould possess an extended half-live in vivo. Polypeptides can alsoinclude peptoids (N-substituted glycines), in which the side chains areappended to nitrogen atoms along the molecule's backbone, rather than tothe α-carbons, as in amino acids. Polypeptides and peptides are intendedto be used interchangeably throughout this application, i.e. where theterm peptide is used, it may also include polypeptides and where theterm polypeptides is used, it may also include peptides.

A vesicle may be isolated, captured or detected using a binding agent.The binding agent can be an agent that binds a vesicle “housekeepingprotein,” or general vesicle biomarker. The biomarker can be CD63, CD9,CD81, CD82, CD37, CD53, Rab-5b, Annexin V or MFG-E8. Tetraspanins, afamily of membrane proteins with four transmembrane domains, can be usedas general vesicle markers. The tetraspanins include CD151, CD53, CD37,CD82, CD81, CD9 and CD63. There have been over 30 tetraspaninsidentified in mammals, including the TSPAN1 (TSP-1), TSPAN2 (TSP-2),TSPAN3 (TSP-3), TSPAN4 (TSP-4, NAG-2), TSPAN5 (TSP-5), TSPAN6 (TSP-6),TSPAN7 (CD231, TALLA-1, A15), TSPAN8 (CO-029), TSPAN9 (NET-5), TSPAN10(Oculospanin), TSPAN11 (CD151-like), TSPAN12 (NET-2), TSPAN13 (NET-6),TSPAN14, TSPAN15 (NET-7), TSPAN16 (TM4-B), TSPAN17, TSPAN18, TSPAN19,TSPAN20 (UP1b, UPK1B), TSPAN21 (UP1a, UPK1A), TSPAN22 (RDS, PRPH2),TSPAN23 (ROM1), TSPAN24 (CD151), TSPAN25 (CD53), TSPAN26 (CD37), TSPAN27(CD82), TSPAN28 (CD81), TSPAN29 (CD9), TSPAN30 (CD63), TSPAN31 (SAS),TSPAN32 (TSSC6), TSPAN33, and TSPAN34. Other commonly observed vesiclemarkers include those listed in Table 3. Any of these proteins can beused as vesicle markers.

TABLE 3 Proteins Observed in Vesicles from Multiple Cell Types ClassProtein Antigen Presentation MHC class I, MHC class II, Integrins, Alpha4 beta 1, Alpha M beta 2, Beta 2 Immunoglobulin ICAM1/CD54, P-selectionfamily Cell-surface peptidases Dipeptidylpeptidase IV/CD26,Aminopeptidase n/CD13 Tetraspanins CD151, CD53, CD37, CD82, CD81, CD9and CD63 Heat-shock proteins Hsp70, Hsp84/90 Cytoskeletal proteinsActin, Actin-binding proteins, Tubulin Membrane transport Annexin I,Annexin II, Annexin IV, Annexin V, and fusion Annexin VI,RAB7/RAP1B/RADGDI Signal transduction Gi2alpha/14-3-3, CBL/LCK Abundantmembrane CD63, GAPDH, CD9, CD81, ANXA2, ENO1, proteins SDCBP, MSN,MFGE8, EZR, GK, ANXA1, LAMP2, DPP4, TSG101, HSPA1A, GDI2, CLTC, LAMP1,Cd86, ANPEP, TFRC, SLC3A2, RDX, RAP1B, RAB5C, RAB5B, MYH9, ICAM1, FN1,RAB11B, PIGR, LGALS3, ITGB1, EHD1, CLIC1, ATP1A1, ARF1, RAP1A, P4HB,MUC1, KRT10, HLA-A, FLOT1, CD59, C1orf58, BASP1, TACSTD1, STOM

The binding agent can also be an agent that binds to a vesicle derivedfrom a specific cell type, such as a tumor cell (e.g. binding agent forTissue factor, EpCam, B7H3 or CD24) or a specific cell-of-origin. Thebinding agent used to isolate or detect a vesicle can be a binding agentfor an antigen selected from FIG. 1. The binding agent for a vesicle canalso be selected from those listed in FIG. 2. The binding agent can befor an antigen such as a tetraspanin, MFG-E8, Annexin V, 5T4, B7H3,caveolin, CD63, CD9, E-Cadherin, Tissue factor, MFG-E8, TMEM211, CD24,PSCA, PCSA, PSMA, Rab-5B, STEAP, TNFR1, CD81, EpCam, CD59, CD81, ICAM,EGFR, or CD66. A binding agent for a platelet can be a glycoprotein suchas GpIa-IIa, GpIIb-IIIa, GpIIIb, GpIb, or GpIX. One or more bindingagents, such as one or more binding agents for two or more of theantigens, can be used for isolating or detecting a vesicle. The bindingagent used can be selected based on the desire of isolating or detectinga vesicle derived from a particular cell type or cell-of-origin specificvesicle.

A binding agent can also be linked directly or indirectly to a solidsurface or substrate. A solid surface or substrate can be any physicallyseparable solid to which a binding agent can be directly or indirectlyattached including, but not limited to, surfaces provided by microarraysand wells, particles such as beads, columns, optical fibers, wipes,glass and modified or functionalized glass, quartz, mica, diazotizedmembranes (paper or nylon), polyformaldehyde, cellulose, celluloseacetate, paper, ceramics, metals, metalloids, semiconductive materials,quantum dots, coated beads or particles, other chromatographicmaterials, magnetic particles; plastics (including acrylics,polystyrene, copolymers of styrene or other materials, polypropylene,polyethylene, polybutylene, polyurethanes, TEFLON™, etc.),polysaccharides, nylon or nitrocellulose, resins, silica or silica-basedmaterials including silicon and modified silicon, carbon, metals,inorganic glasses, plastics, ceramics, conducting polymers (includingpolymers such as polypyrole and polyindole); micro or nanostructuredsurfaces such as nucleic acid tiling arrays, nanotube, nanowire, ornanoparticulate decorated surfaces; or porous surfaces or gels such asmethacrylates, acrylamides, sugar polymers, cellulose, silicates, orother fibrous or stranded polymers. In addition, as is known the art,the substrate may be coated using passive or chemically-derivatizedcoatings with any number of materials, including polymers, such asdextrans, acrylamides, gelatins or agarose. Such coatings can facilitatethe use of the array with a biological sample.

For example, an antibody used to isolate a vesicle can be bound to asolid substrate such as a well, such as commercially available plates(e.g. from Nunc, Milan Italy). Each well can be coated with theantibody. In some embodiments, the antibody used to isolate a vesicle isbound to a solid substrate such as an array. The array can have apredetermined spatial arrangement of molecule interactions, bindingislands, biomolecules, zones, domains or spatial arrangements of bindingislands or binding agents deposited within discrete boundaries. Further,the term array may be used herein to refer to multiple arrays arrangedon a surface, such as would be the case where a surface bore multiplecopies of an array. Such surfaces bearing multiple arrays may also bereferred to as multiple arrays or repeating arrays.

A binding agent can also be bound to particles such as beads ormicrospheres. For example, an antibody specific for a component of avesicle can be bound to a particle, and the antibody-bound particle isused to isolate a vesicle from a biological sample. In some embodiments,the microspheres may be magnetic or fluorescently labeled. In addition,a binding agent for isolating vesicles can be a solid substrate itself.For example, latex beads, such as aldehyde/sulfate beads (InterfacialDynamics, Portland, Oreg.) can be used.

A binding agent bound to a magnetic bead can also be used to isolate avesicle. For example, a biological sample such as serum from a patientcan be collected for colon cancer screening. The sample can be incubatedwith anti-CCSA-3 (Colon Cancer-Specific Antigen) coupled to magneticmicrobeads. A low-density microcolumn can be placed in the magneticfield of a MACS Separator and the column is then washed with a buffersolution such as Tris-buffered saline. The magnetic immune complexes canthen be applied to the column and unbound, non-specific material can bediscarded. The CCSA-3 selected vesicle can be recovered by removing thecolumn from the separator and placing it on a collection tube. A buffercan be added to the column and the magnetically labeled vesicle can bereleased by applying the plunger supplied with the column. The isolatedvesicle can be diluted in IgG elution buffer and the complex can then becentrifuged to separate the microbeads from the vesicle. The pelletedisolated cell-of-origin specific vesicle can be resuspended in buffersuch as phosphate-buffered saline and quantitated. Alternatively, due tothe strong adhesion force between the antibody captured cell-of-originspecific vesicle and the magnetic microbeads, a proteolytic enzyme suchas trypsin can be used for the release of captured vesicles without theneed for centrifugation. The proteolytic enzyme can be incubated withthe antibody captured cell-of-origin specific vesicles for at least atime sufficient to release the vesicles.

A binding agent, such as an antibody, for isolating vesicles ispreferably contacted with the biological sample comprising the vesiclesof interest for at least a time sufficient for the binding agent to bindto a component of the vesicle. For example, an antibody may be contactedwith a biological sample for various intervals ranging from secondsdays, including but not limited to, about 10 minutes, 30 minutes, 1hour, 3 hours, 5 hours, 7 hours, 10 hours, 15 hours, 1 day, 3 days, 7days or 10 days.

A binding agent, such as an antibody specific to an antigen listed inFIG. 1, or a binding agent listed in FIG. 2, can be labeled with,including but not limited to, a magnetic label, a fluorescent moiety, anenzyme, a chemiluminescent probe, a metal particle, a non-metalcolloidal particle, a polymeric dye particle, a pigment molecule, apigment particle, an electrochemically active species, semiconductornanocrystal or other nanoparticles including quantum dots or goldparticles. The label can be, but not be limited to, fluorophores,quantum dots, or radioactive labels. For example, the label can be aradioisotope (radionuclides), such as ³H, ¹¹C, ¹⁴C, ¹⁸F, ³²F, ³⁵S, ⁶⁴Cu,⁶⁸Ga, ⁸⁶Y, ⁹⁹Te, ¹¹¹In, ¹²³I, ¹²⁴I, ¹²⁵I, ¹³¹I, ¹³³Xe, ¹⁷⁷Lu, ²¹¹At, or²¹³Bi. The label can be a fluorescent label, such as a rare earthchelate (europium chelate), fluorescein type, such as, but not limitedto, FITC, 5-carboxyfluorescein, 6-carboxy fluorescein; a rhodamine type,such as, but not limited to, TAMRA; dansyl; Lissamine; cyanines;phycoerythrins; Texas Red; and analogs thereof. The fluorescent labelcan be one or more of FAM, dRHO, 5-FAM, 6FAM, dR6G, JOE, HEX, VIC, TET,dTAMRA, TAMRA, NED, dROX, PET, BHQ, Gold540 and LIZ.

A binding agent can be directly or indirectly labeled, e.g., the labelis attached to the antibody through biotin-streptavidin. Alternatively,an antibody is not labeled, but is later contacted with a secondantibody that is labeled after the first antibody is bound to an antigenof interest.

For example, various enzyme-substrate labels are available or disclosed(see for example, U.S. Pat. No. 4,275,149). The enzyme generallycatalyzes a chemical alteration of a chromogenic substrate that can bemeasured using various techniques. For example, the enzyme may catalyzea color change in a substrate, which can be measuredspectrophotometrically. Alternatively, the enzyme may alter thefluorescence or chemiluminescence of the substrate. Examples ofenzymatic labels include luciferases (e.g., firefly luciferase andbacterial luciferase; U.S. Pat. No. 4,737,456), luciferin,2,3-dihydrophthalazinediones, malate dehydrogenase, urease, peroxidasesuch as horseradish peroxidase (HRP), alkaline phosphatase (AP),β-galactosidase, glucoamylase, lysozyme, saccharide oxidases (e.g.,glucose oxidase, galactose oxidase, and glucose-6-phosphatedehydrogenase), heterocyclic oxidases (such as uricase and xanthineoxidase), lactoperoxidase, microperoxidase, and the like. Examples ofenzyme-substrate combinations include, but are not limited to,horseradish peroxidase (HRP) with hydrogen peroxidase as a substrate,wherein the hydrogen peroxidase oxidizes a dye precursor (e.g.,orthophenylene diamine (OPD) or 3,3′,5,5′-tetramethylbenzidinehydrochloride (TMB)); alkaline phosphatase (AP) with para-nitrophenylphosphate as chromogenic substrate; and β-D-galactosidase (β-D-Gal) witha chromogenic substrate (e.g., p-nitrophenyl-β-D-galactosidase) orfluorogenic substrate 4-methylumbelliferyl-β-D-galactosidase.

Depending on the method of isolation or detection used, the bindingagent may be linked to a solid surface or substrate, such as arrays,particles, wells and other substrates described above. Methods fordirect chemical coupling of antibodies, to the cell surface are known inthe art, and may include, for example, coupling using glutaraldehyde ormaleimide activated antibodies. Methods for chemical coupling usingmultiple step procedures include biotinylation, coupling oftrinitrophenol (TNP) or digoxigenin using for example succinimide estersof these compounds. Biotinylation can be accomplished by, for example,the use of D-biotinyl-N-hydroxysuccinimide. Succinimide groups reacteffectively with amino groups at pH values above 7, and preferentiallybetween about pH 8.0 and about pH 8.5. Biotinylation can be accomplishedby, for example, treating the cells with dithiothreitol followed by theaddition of biotin maleimide.

Flow Cytometry

Isolation or detection of a vesicle using a particle such as a bead ormicrosphere can also be performed using flow cytometry. Flow cytometrycan be used for sorting microscopic particles suspended in a stream offluid. As particles pass through they can be selectively charged and ontheir exit can be deflected into separate paths of flow. It is thereforepossible to separate populations from an original mix, such as abiological sample, with a high degree of accuracy and speed. Flowcytometry allows simultaneous multiparametric analysis of the physicaland/or chemical characteristics of single cells flowing through anoptical/electronic detection apparatus. A beam of light, usually laserlight, of a single frequency (color) is directed onto a hydrodynamicallyfocused stream of fluid. A number of detectors are aimed at the pointwhere the stream passes through the light beam; one in line with thelight beam (Forward Scatter or FSC) and several perpendicular to it(Side Scatter or SSC) and one or more fluorescent detectors.

Each suspended particle passing through the beam scatters the light insome way, and fluorescent chemicals in the particle may be excited intoemitting light at a lower frequency than the light source. Thiscombination of scattered and fluorescent light is picked up by thedetectors, and by analyzing fluctuations in brightness at each detector(one for each fluorescent emission peak), it is possible to deducevarious facts about the physical and chemical structure of eachindividual particle. FSC correlates with the cell size and SSC dependson the inner complexity of the particle, such as shape of the nucleus,the amount and type of cytoplasmic granules or the membrane roughness.Some flow cytometers have eliminated the need for fluorescence and useonly light scatter for measurement.

Flow cytometers can analyze several thousand particles every second in“real time” and can actively separate out and isolate particles havingspecified properties. They offer high-throughput automatedquantification, and separation, of the set parameters for a high numberof single cells during each analysis session. Flow cytomers can havemultiple lasers and fluorescence detectors, allowing multiple labels tobe used to more precisely specify a target population by theirphenotype. Thus, a flow cytometer, such as a multicolor flow cytometer,can be used to detect one or more vesicles with multiple fluorescentlabels or colors. In some embodiments, the flow cytometer can also sortor isolate different vesicle populations, such as by size or bydifferent markers.

The flow cytometer may have one or more lasers, such as 1, 2, 3, 4, 5,6, 7, 8, 9, 10 or more lasers. In some embodiments, the flow cytometercan detect more than one color or fluorescent label, such as at least 2,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20different colors or fluorescent labels. For example, the flow cytometercan have at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, or 20 fluorescence detectors.

Examples of commerically available flow cytometers that can be used todetect or analyze one or more vesicles, to sort or separate differentpopulations of vesicles, include, but are not limited to the MoFlo™ XDPCell Sorter (Beckman Coulter, Brea, Calif.), MoFlo™ Legacy Cell Sorter(Beckman Coulter, Brea, Calif.), BD FACSAria™ Cell Sorter (BDBiosciences, San Jose, Calif.), BD™ LSRII (BD Biosciences, San Jose,Calif.), and BD FACSCalibur™ (BD Biosciences, San Jose, Calif.). Use ofmulticolor or multi-fluor cytometers can be used in multiplex analysisof vesicles, as further described below. In some embodiments, the flowcytometer can sort, and thereby collect or sort more than one populationof vesicles based one or more characteristics. For example, twopopulations of vesicles differ in size, such that the vesicles withineach population have a similar size range and can be differentiallydetected or sorted. In another embodiment, two different populations ofvesicles are differentially labeled.

The data resulting from flow-cytometers can be plotted in 1 dimension toproduce histograms or seen in 2 dimensions as dot plots or in 3dimensions with newer software. The regions on these plots can besequentially separated by a series of subset extractions which aretermed gates. Specific gating protocols exist for diagnostic andclinical purposes especially in relation to hematology. The plots areoften made on logarithmic scales. Because different fluorescent dye'semission spectra overlap, signals at the detectors have to becompensated electronically as well as computationally. Fluorophores forlabeling biomarkers may include those described in Ormerod, FlowCytometry 2nd ed., Springer-Verlag, New York (1999), and in Nida et al.,Gynecologic Oncology 2005; 4 889-894 which is incorporated herein byreference.

Multiplexing

Multiplex experiments comprise experiments that can simultaneouslymeasure multiple analytes in a single assay. Vesicles and associatedbiomarkers can be assessed in a multiplex fashion. Different bindingagents can be used for multiplexing different vesicle populations.Different vesicle populations can be isolated or detected usingdifferent binding agents. Each population in a biological sample can belabeled with a different signaling label, such as a fluorophore, quantumdot, or radioactive label, such as described above. The label can bedirectly conjugated to a binding agent or indirectly used to detect abinding agent that binds a vesicle. The number of populations detectedin a multiplexing assay is dependent on the resolution capability of thelabels and the summation of signals, as more than two differentiallylabeled vesicle populations that bind two or more affinity elements canproduce summed signals.

Multiplexing of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,16, 17, 18, 19, 20, 25, 50, 75 or 100 different vesicle populations maybe performed. For example, one population of vesicles specific to acell-of-origin can be assayed along with a second population of vesiclesspecific to a different cell-of-origin, where each population is labeledwith a different label. Alternatively, a population of vesicles with aparticular biomarker or biosignature can be assayed along with a secondpopulation of vesicles with a different biomarker or biosignature. Insome cases, hundreds or thousands of vesicles are assessed in a singleassay.

In one embodiment, multiplex analysis is performed by applying aplurality of vesicles comprising more than one population of vesicles toa plurality of substrates, such as beads. Each bead is coupled to one ormore capture agents. The plurality of beads is divided into subsets,where beads with the same capture agent or combination of capture agentsform a subset of beads, such that each subset of beads has a differentcapture agent or combination of capture agents than another subset ofbeads. The beads can then be used to capture vesicles that comprise acomponent that binds to the capture agent. The different subsets can beused to capture different populations of vesicles. The captured vesiclescan then be analyzed by detecting one or more biomarkers.

Flow cytometry can be used in combination with a particle-based or beadbased assay. Multiparametric immunoassays or other high throughputdetection assays using bead coatings with cognate ligands and reportermolecules with specific activities consistent with high sensitivityautomation can be used. For example, beads in each subset can bedifferentially labeled from another subset. In a particle based assaysystem, a binding agent or capture agent for a vesicle, such as acapture antibody, can be immobilized on addressable beads ormicrospheres. Each binding agent for each individual binding assay (suchas an immunoassay when the binding agent is an antibody) can be coupledto a distinct type of microsphere (i.e., microbead) and the bindingassay reaction takes place on the surface of the microspheres.Microspheres can be distinguished by different labels, for example, amicrosphere with a specific capture agent would have a differentsignaling label as compared to another microsphere with a differentcapture agent. For example, microspheres can be dyed with discretefluorescence intensities such that the fluorescence intensity of amicrosphere with a specific binding agent is different than that ofanother microsphere with a different binding agent.

The microsphere can be labeled or dyed with at least 2 different labelsor dyes. In some embodiments, the microsphere is labeled with at least3, 4, 5, 6, 7, 8, 9, or 10 different labels. Different microspheres in aplurality of microspheres can have more than one label or dye, whereinvarious subsets of the microspheres have various ratios and combinationsof the labels or dyes permitting detection of different microsphereswith different binding agents. For example, the various ratios andcombinations of labels and dyes can permit different fluorescentintensities. Alternatively, the various ratios and combinations maybeused to generate different detection patters to identify the bindingagent. The microspheres can be labeled or dyed externally or may haveintrinsic fluorescence or signaling labels. Beads can be loadedseparately with their appropriate binding agents and thus, differentvesicle populations can be isolated based on the different bindingagents on the differentially labeled microspheres to which the differentbinding agents are coupled.

In another embodiment, multiplex analysis can be performed using aplanar substrate, wherein the substrate comprises a plurality of captureagents. The plurality of capture agents can capture one or morepopulations of vesicles, and one or more biomarkers of the capturedvesicles detected. The planar substrate can be a microarray or othersubstrate as further described herein.

Novel Binding Agents

A vesicle may be isolated or detected using a binding agent for a novelcomponent of a vesicle, such as an antibody for a novel antigen specificto a vesicle of interest. Novel antigens that are specific to a vesicleof interest may be isolated or identified using different test compoundsof known composition bound to a substrate, such as an array or aplurality of particles, which can allow a large amount ofchemical/structural space to be adequately sampled using only a smallfraction of the space. The novel antigen identified can also serve as abiomarker for the vesicle. For example, a novel antigen identified for acell-of-origin specific vesicle can be a useful biomarker.

A binding agent can be identified by screening either a homogeneous orheterogeneous vesicle population against test compounds. Since thecomposition of each test compound on the substrate surface is known,this constitutes a screen for affinity elements. For example, a testcompound array comprises test compounds at specific locations on thesubstrate addressable locations, and can be used to identify one or morebinding agents for a vesicle. The test compounds can all be unrelated orrelated based on minor variations of a core sequence or structure. Thedifferent test compounds may include variants of a given test compound(such as polypeptide isoforms), test compounds that are structurally orcompositionally unrelated, or a combination thereof.

A test compound can be a peptoid, polysaccharide, organic compound,inorganic compound, polymer, lipids, nucleic acid, polypeptide,antibody, protein, polysaccharide, or other compound. The test compoundcan be natural or synthetic. The test compound can comprise or consistof linear or branched heteropolymeric compounds based on any of a numberof linkages or combinations of linkages (e.g., amide, ester, ether,thiol, radical additions, metal coordination, etc.), dendriticstructures, circular structures, cavity structures or other structureswith multiple nearby sites of attachment that serve as scaffolds uponwhich specific additions are made. Thes test compound can be spotted ona substrate or synthesized in situ, using standard methods in the art.In addition, the test compound can be spotted or synthesized in situ incombinations in order to detect useful interactions, such as cooperativebinding.

The test compound can be a polypeptide with known amino acid sequence,thus, detection of a test compound binding with a vesicle can lead toidentification of a polypeptide of known amino sequence that can be usedas a binding agent. For example, a homogenous population of vesicles canbe applied to a spotted array on a slide containing between a few and1,000,000 test polypeptides having a length of variable amino acids. Thepolypeptides can be attached to the surface through the C-terminus. Thesequence of the polypeptides can be generated randomly from 19 aminoacids, excluding cysteine. The binding reaction can include anon-specific competitor, such as excess bacterial proteins labeled withanother dye such that the specificity ratio for each polypeptide bindingtarget can be determined. The polypeptides with the highest specificityand binding can be selected. The identity of the polypeptide on eachspot is known, and thus can be readily identified. Once the novelantigens specific to the homogeneous vesicle population, such as acell-of-origin specific vesicle is identified, such cell-of-originspecific vesicles may subsequently be isolated using such antigens inmethods described hereafter.

An array can also be used for identifying an antibody as a binding agentfor a vesicle. Test antibodies can be attached to an array and screenedagainst a heterogeneous population of vesicles to identify antibodiesthat can be used to isolate or identify a vesicle. A homogeneouspopulation of vesicles such as cell-of-origin specific vesicles can alsobe screened with an antibody array. Other than identifying antibodies toisolate or detect a homogeneous population of vesicles, one or moreprotein biomarkers specific to the homogenous population can beidentified. Commercially available platforms with test antibodiespre-selected or custom selection of test antibodies attached to thearray can be used. For example, an antibody array from Full MoonBiosystems can be screened using prostate cancer cell derived vesiclesidentifying antibodies to Bcl-XL, ERCC1, Keratin 15, CD81/TAPA-1, CD9,Epithelial Specific Antigen (ESA), and Mast Cell Chymase as bindingagents (see for example, FIG. 62), and the proteins identified can beused as biomarkers for the vesicles.

An antibody or synthetic antibody to be used as a binding agent can alsobe identified through a peptide array. Another method is the use ofsynthetic antibody generation through antibody phage display. M13bacteriophage libraries of antibodies (e.g. Fabs) are displayed on thesurfaces of phage particles as fusions to a coat protein. Each phageparticle displays a unique antibody and also encapsulates a vector thatcontains the encoding DNA. Highly diverse libraries can be constructedand represented as phage pools, which can be used in antibody selectionfor binding to immobilized antigens. Antigen-binding phages are retainedby the immobilized antigen, and the nonbinding phages are removed bywashing. The retained phage pool can be amplified by infection of anEscherichia coli host and the amplified pool can be used for additionalrounds of selection to eventually obtain a population that is dominatedby antigen-binding clones. At this stage, individual phase clones can beisolated and subjected to DNA sequencing to decode the sequences of thedisplayed antibodies. Through the use of phase display and other methodsknown in the art, high affinity designer antibodies for vesicles can begenerated.

Bead-based assays can also be used to identify novel binding agents toisolate or detect a vesicle. A test antibody or peptide can beconjugated to a particle. For example, a bead can be conjugated to anantibody or peptide and used to detect and quantify the proteinsexpressed on the surface of a population of vesicles in order todiscover and specifically select for novel antibodies that can targetvesicles from specific tissue or tumor types. Any molecule of organicorigin can be successfully conjugated to a polystyrene bead through useof a commercially available kit according to manufacturer'sinstructions. Each bead set can be colored a certain detectablewavelength and each can be linked to a known antibody or peptide whichcan be used to specifically measure which beads are linked to exosomalproteins matching the epitope of previously conjugated antibodies orpeptides. The beads can be dyed with discrete fluorescence intensitiessuch that each bead with a different intensity has a different bindingagent as described above.

For example, a purified vesicle preparation can be diluted in assaybuffer to an appropriate concentration according to empiricallydetermined dynamic range of assay. A sufficient volume of coupled beadscan be prepared and approximately 1 μl of the antibody-coupled beads canbe aliqouted into a well and adjusted to a final volume of approximately504 Once the antibody-conjugated beads have been added to a vacuumcompatible plate, the beads can be washed to ensure proper bindingconditions. An appropriate volume of vesicle preparation can then beadded to each well being tested and the mixture incubated, such as for15-18 hours. A sufficient volume of detection antibodies using detectionantibody diluent solution can be prepared and incubated with the mixturefor 1 hour or for as long as necessary. The beads can then be washedbefore the addition of detection antibody (biotin expressing) mixturecomposed of streptavidin phycoereythin. The beads can then be washed andvacuum aspirated several times before analysis on a suspension arraysystem using software provided with an instrument. The identity ofantigens that can be used to selectively extract the vesicles can thenbe elucidated from the analysis.

Assays using imaging systems can be utilized to detect and quantifyproteins expressed on the surface of a vesicle in order to discover andspecifically select for and enrich vesicles from specific tissue, cellor tumor types. Antibodies, peptides or cells conjugated to multiplewell multiplex carbon coated plates can be used. Simultaneousmeasurement of many analytes in a well can be achieved through the useof capture antibodies arrayed on the patterned carbon working surface.Analytes can then be detected with antibodies labeled with reagents inelectrode wells with an enhanced electro-chemiluminescent plate. Anymolecule of organic origin can be successfully conjugated to the carboncoated plate. Proteins expressed on the surface of vesicles can beidentified from this assay and can be used as targets to specificallyselect for and enrich vesicles from specific tissue or tumor types.

The binding agent can also be an aptamer, which refers to nucleic acidsthat can bond molecules other than their complementary sequence. Anaptamer typically contains 30-80 nucleic acids and can have a highaffinity towards a certain target molecule (K_(d)'s reported are between10⁻¹¹-10⁻⁶ mole/1). An aptamer for a target can be identified usingsystematic evolution of ligands by exponential enrichment (SELEX) (Tuerk& Gold, Science 249:505-510, 1990; Ellington & Szostak, Nature346:818-822, 1990), such as described in U.S. Pat. Nos. 5,270,163,6,482, 594, 6,291, 184, 6,376, 190 and U.S. Pat. No. 6,458,539. Alibrary of nucleic acids can be contacted with a target vesicle, andthose nucleic acids specifically bound to the target are partitionedfrom the remainder of nucleic acids in the library which do notspecifically bind the target. The partitioned nucleic acids areamplified to yield a ligand-enriched pool. Multiple cycles of binding,partitioning, and amplifying (i.e., selection) result in identificationof one or more aptamers with the desired activity. Another method foridentifying an aptamer to isolate vesicles is described in U.S. Pat. No.6,376,190, which describes increasing or decreasing frequency of nucleicacids in a library by their binding to a chemically synthesized peptide.Modified methods, such as Laser SELEX or deSELEX as described in U.S.Patent Publication No. 20090264508 can also be used.

Microfluidics

The methods for isolating or identifying vesicles can be used incombination with microfluidic devices. The methods of isolating ordetecting a vesicle, such as described herien, can be performed using amicrofluidic device. Microfluidic devices, which may also be referred toas “lab-on-a-chip” systems, biomedical micro-electro-mechanical systems(bioMEMs), or multicomponent integrated systems, can be used forisolating and analyzing a vesicle. Such systems miniaturize andcompartmentalize processes that allow for binding of vesicles, detectionof biosignatures, and other processes.

A microfluidic device can also be used for isolation of a vesiclethrough size differential or affinity selection. For example, amicrofluidic device can use one more channels for isolating a vesiclefrom a biological sample based on size or by using one or more bindingagents for isolating a vesicle from a biological sample. A biologicalsample can be introduced into one or more microfluidic channels, whichselectively allows the passage of a vesicle. The selection can be basedon a property of the vesicle, such as the size, shape, deformability, orbiosignature of the vesicle.

In one embodiment, a heterogeneous population of vesicles can beintroduced into a microfluidic device, and one or more differenthomogeneous populations of vesicles can be obtained. For example,different channels can have different size selections or binding agentsto select for different vesicle populations. Thus, a microfluidic devicecan isolate a plurality of vesicles wherein at least a subset of theplurality of vesicles comprises a different biosignature from anothersubset of the plurality of vesicles. For example, the microfluidicdevice can isolate at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30,40, 50, 60, 70, 80, 90, or 100 different subsets of vesicles, whereineach subset of vesicles comprises a different biosignature.

In some embodiments, the microfluidic device can comprise one or morechannels that permit further enrichment or selection of a vesicle. Apopulation of vesicles that has been enriched after passage through afirst channel can be introduced into a second channel, which allows thepassage of the desired vesicle or vesicle population to be furtherenriched, such as through one or more binding agents present in thesecond channel.

Array-based assays and bead-based assays can be used with microfluidicdevice. For example, the binding agent can be coupled to beads and thebinding reaction between the beads and vesicle can be performed in amicrofluidic device. Multiplexing can also be performed using amicrofluidic device. Different compartments can comprise differentbinding agents for different populations of vesicles, where eachpopulation is of a different cell-of-origin specific vesicle population.In one embodiment, each population has a different biosignature. Thehybridization reaction between the microsphere and vesicle can beperformed in a microfluidic device and the reaction mixture can bedelivered to a detection device. The detection device, such as a dual ormultiple laser detection system can be part of the microfluidic systemand can use a laser to identify each bead or microsphere by itscolor-coding, and another laser can detect the hybridization signalassociated with each bead.

Examples of microfluidic devices that may be used, or adapted for usewith vesicles, include but are not limited to those described in U.S.Pat. Nos. 7,591,936, 7,581,429, 7,579,136, 7,575,722, 7,568,399,7,552,741, 7,544,506, 7,541,578, 7,518,726, 7,488,596, 7,485,214,7,467,928, 7,452,713, 7,452,509, 7,449,096, 7,431,887, 7,422,725,7,422,669, 7,419,822, 7,419,639, 7,413,709, 7,411,184, 7,402,229,7,390,463, 7,381,471, 7,357,864, 7,351,592, 7,351,380, 7,338,637,7,329,391, 7,323,140, 7,261,824, 7,258,837, 7,253,003, 7,238,324,7,238,255, 7,233,865, 7,229,538, 7,201,881, 7,195,986, 7,189,581,7,189,580, 7,189,368, 7,141,978, 7,138,062, 7,135,147, 7,125,711,7,118,910, 7,118,661, 7,640,947, 7,666,361, 7,704,735; and InternationalPatent Publication WO 2010/072410; each of which patents or applicationsare incorporated herein by reference in their entirety. Another examplefor use with methods disclosed herein is described in Chen et al.,“Microfluidic isolation and transcriptome analysis of serum vesicles,”Lab on a Chip, Dec. 8, 2009 DOI: 10. 1039/b916199f.

In one embodiment, a microfluidic device for isolating or detecting avesicle comprises a channel of less than about 1, 2, 3, 4, 5, 6, 7, 8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,27, 28, 29, 30, 35, 40, 45, 50, 55, of 60 mm in width, or between about2-60, 3-50, 3-40, 3-30, 3-20, or 4-20 mm in width. The microchannel canhave a depth of less than about 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,38, 39, 40, 45, 50, 55, 60, 65 or 70 μm, or between about 10-70, 10-40,15-35, or 20-30 μm. Furthermore, the microchannel can have a length ofless than about 1, 2, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9,9.5 or 10 cm. The microfluidic device can have grooves on its ceilingthat are less than about 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,52, 53, 54, 55, 56, 57, 58, 59, 6, 65, 70, 75, or 80 μm wide, or betweenabout 40-80, 40-70, 40-60 or 45-55 μm wide. The grooves can be less thanabout 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 25, 30, 35, 40, 45, or 50 μm deep, such as between about 1-50, 5-40,5-30, 3-20 or 5-15 μm.

The microfluidic device can have one or more binding agents attached toa surface in a channel, or present in a channel. For example, themicrochannel can have one or more capture agents, such as a captureagent for EpCam, CD9, PCSA, CD63, CD81, PSMA, B7H3, PSCA, ICAM, STEAP,and EGFR. In one embodiment, a microchannel surface is treated withavidin and a capture agent, such as an antibody, that is biotinylatedcan be injected into the channel to bind the avidin.

A biological sample can be flowed into the microfluidic device, or amicrochannel, at rates such as at least about 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, or 50 μlper minute, such as between about 1-50, 5-40, 5-30, 3-20 or 5-15 μl perminute. One or more vesicles can be captured and directly detected inthe microfludic device. Alternatively, the captured vesicle may bereleased and exit the microfluidic device prior to analysis. In anotherembodiment, one or more captured vesicles are lysed in the microchanneland the lysate can be analyzed. Lysis buffer can be flowed through thechannel and lyse the captured vesicles. For example, the lysis buffercan be flowed into the device or microchannel at rates such as at leastabout a, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 25, 26, 27, 28, 29, 30, 35, 40, 45, or 50 μl per minute, such asbetween about 1-50, 5-40, 10-30, 5-30 or 10-35 μl per minute. The lysatecan be collected and analyzed, such as performing RT-PCR, PCR, massspectrometry, Western blotting, or other assays, to detect one or morebiomarkers of the vesicle.

Cell-of-Origin and Disease-Specific Vesicles

The bindings agent disclosed herein can be used to isolate or detect avesicle, such as a cell-of-origin vesicle or vesicle with a specificbiosignature. The binding agent can be used to isolate or detect aheterogeneous population of vesicles from a sample or can be used toisolate or detect a homogeneous population of vesicles, such ascell-of-origin specific vesicles with specific biosignatures, from aheterogeneous population of vesicles.

A homogeneous population of vesicles, such as cell-of-origin specificvesicles, can be analyzed and used to characterize a phenotype for asubject. Cell-of-origin specific vesicles are esicles derived fromspecific cell types, which can include, but are not limited to, cells ofa specific tissue, cells from a specific tumor of interest or a diseasedtissue of interest, circulating tumor cells, or cells of maternal orfetal origin. The vesicles may be derived from tumor cells or lung,pancreas, stomach, intestine, bladder, kidney, ovary, testis, skin,colorectal, breast, prostate, brain, esophagus, liver, placenta, orfetal cells. The isolated vesicle can also be from a particular sampletype, such as urinary vesicle.

A cell-of-origin specific vesicle from a biological sample can beisolated using one or more binding agents that are specific to acell-of-origin. Vesicles for analysis of a disease or condition can beisolated using one or more binding agent specific for biomarkers forthat disease or condition.

A vesicle can be concentrated prior to isolation or detection of acell-of-origin specific vesicle, such as through centrifugation,chromatography, or filtration, as described above, to produce aheterogeneous population of vesicles prior to isolation ofcell-of-origin specific vesicles. Alternatively, the vesicle is notconcentrated, or the biological sample is not enriched for a vesicle,prior to isolation of a cell-of-origin vesicle.

FIG. 61B illustrates a flowchart which depicts one method 6100B forisolating or identifying a cell-of-origin specific vesicle. First, abiological sample is obtained from a subject in step 6102. The samplecan be obtained from a third party or from the same party performing theanalysis. Next, cell-of-origin specific vesicles are isolated from thebiological sample in step 6104. The isolated cell-of-origin specificvesicles are then analyzed in step 6106 and a biomarker or biosignaturefor a particular phenotype is identified in step 6108. The method may beused for a number of phenotypes. In some embodiments, prior to step6104, vesicles are concentrated or isolated from a biological sample toproduce a homogeneous population of vesicles. For example, aheterogeneous population of vesicles may be isolated usingcentrifugation, chromatography, filtration, or other methods asdescribed above, prior to use of one or more binding agents specific forisolating or identifying vesicles derived from specific cell types.

A cell-of-origin specific vesicle can be isolated from a biologicalsample of a subject by employing one or more binding agents that bindwith high specificity to the cell-of-origin specific vesicle. In someinstances, a single binding agent can be employed to isolate acell-of-origin specific vesicle. In other instances, a combination ofbinding agents may be employed to isolate a cell-of-origin specificvesicle. For example, at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, 16, 17, 18, 19, 20, 25, 50, 75, or 100 different binding agentsmay be used to isolate a cell-of-origin vesicle. Therefore, a vesiclepopulation (e.g., vesicles having the same binding agent profile) can beidentified by utilizing a single or a plurality of binding agents.

One or more binding agents can be selected based on their specificityfor a target antigen(s) that is specific to a cell-of-origin, e.g., acell-of-origin that is related to a tumor, autoimmune disease,cardiovascular disease, neurological disease, infection or other diseaseor disorder. The cell-of-origin can be from a cell that is informativefor a diagnosis, prognosis, disease stratification, theranosis,prediction of responder/non-responder status, disease monitoring,treatment monitoring and the like as related to such diseases anddisorders. The cell-of-origin can also be from a cell useful to discoverbiomarkers for use thereto. Non-limiting examples of antigens which maybe used singularly, or in combination, to isolate a cell-of-originspecific vesicle, disease specific vesicle, or tumor specific vesicle,are shown in FIG. 1 and are also described herein. The antigen cancomprise membrane bound antigens which are accessible to binding agents.The antigen can be a biomarker related to characterizing a phenotype.

One of skill will appreciate that any applicable antigen that can beused to isolate an informative vesicle is contemplated by the invention.Binding agents, e.g., antibodies, aptamers and lectins, can be chosenthat recognize surface antigens and/or fragments thereof, as outlinedherein. The binding agents can recognize antigens specific to thedesired cell type or location and/or recognize biomarkers associatedwith the desired cells. The cells can be, e.g., tumor cells, otherdiseased cells, cells that serve as markers of disease such as activatedimmune cells, etc. One of skill will appreciate that binding agents forany cells of interest can be useful for isolating vesicles associatedwith those cells. One of skill will further appreciate that the bindingagents disclosed herein can be used for detecting vesicles of interest.As a non-limiting example, a binding agent to a vesicle biomarker can belabeled directly or indirectly in order to detect vesicles bound by oneof more of the same or different binding agents.

A number of targets for binding agents useful for binding to vesiclesassociated with cancer, autoimmune diseases, cardiovascular diseases,neurological diseases, infection or other disease or disorders arepresented in Table 4. A vesicle derived from a cell associated with oneof the listed disorders can be characterized using one of the antigensin the table. The binding agent, e.g., an antibody or aptamer, canrecognize an epitope of the listed antigens, a fragment thereof, orbinding agents can be used against any appropriate combination. Otherantigens associated with the disease or disorder can be recognized aswell in order to characterize the vesicle. One of skill will appreciatethat any applicable antigen that can be used to assess an informativevesicle is contemplated by the invention for isolation, capture ordetection in order to characterize a vesicle.

TABLE 4 Illustrative Antigens for Use in Characterizing Various Diseasesand Disorders Disease or disorder Target Breast cancer, e.g., glandularor stromal cells BCA-225, hsp70, MART1, ER, VEGFA, Class III b- tubulin,HER2/neu (for Her2+ breast cancer), GPR30, ErbB4 (JM) isoform, MPR8,MISIIR Ovarian Cancer CA125, VEGFR2, HER2, MISIIR, VEGFA, CD24 LungCancer CYFRA21-1, TPA-M, TPS, CEA, SCC-Ag, XAGE- 1b, HLA Class 1,TA-MUC1, KRAS, hENT1, kinin B1 receptor, kinin B2 receptor, TSC403,HTI56, DC- LAMP Colon Cancer CEA, MUC2, GPA33, CEACAM5, ENFB1, CCSA-3,CCSA-4, ADAM10, CD44, NG2, ephrin B1, plakoglobin, galectin 4, RACK1,tetraspanin-8, FASL, A33, CEA, EGFR, dipeptidase 1, PTEN, Na(+)-dependent glucose transporter, UDP- glucuronosyltransferase 1A ProstateCancer PSA, TMPRSS2, FASLG, TNFSF10, PSMA, NGEP, I1-7RI, CSCR4, CysLT1R,TRPM8, Kv1.3, TRPV6, TRPM8, PSGR, MISIIR, galectin-3, PCA3, TMPRSS2:ERGBrain Cancer PRMT8, BDNF, EGFR, DPPX, Elk, Densin-180, BAI2, BAI3 BloodCancer (hematological malignancy) CD44, CD58, CD31, CD11a, CD49d, GARP,BTS, Raftlin Melanoma DUSP1, TYRP1, SILV, MLANA, MCAM, CD63, Alix,hsp70, meosin, p120 catenin, PGRL, syntaxin binding protein 1 & 2,caveolin Liver Cancer (hepatocellular carcinoma) HBxAg, HBsAg, NLTCervical Cancer MCT-1, MCT-2, MCT-4 Endometrial Cancer Alpha V Beta 6integrin Psoriasis flt-1, VPF receptors, kdr Autoimmune Disease Tim-2Irritable Bowel Disease (IBD or Syndrome (IBS) IL-16, IL-1beta, IL-12,TNF-alpha, interferon-gamma, IL-6, Rantes, II-12, MCP-1, 5HT Diabetes,e.g., pancreatic cells IL-6, CRP, RBP4 Barrett's Esophagus p53, MUC1,MUC6 Fibromyalgia neopterin, gp130 Benign Prostatic Hyperplasia (BPH)KIA1, intact fibronectin Multiple Sclerosis B7, B7-2, CD-95 (fas),Apo-1/Fas Parkinson's Disease PARK2, ceruloplasmin, VDBP, tau, DJ-1Rheumatic Disease Citrulinated fibrin a-chain, CD5 antigen-likefibrinogen fragment D, CD5 antigen-like fibrinogen fragment B, TNF alphaAlzheimer's Disease APP695, APP751 or APP770, BACE1, cystatin C, amyloidβ, T-tau, complement factor H, alpha-2- macroglobulin Head and NeckCancer EGFR, EphB4, Ephrin B2 Gastrointestinal Stromal Tumor (GIST)c-kit PDGFRA, NHE-3 Renal Cell Carcinoma c PDGFRA, VEGF, HIF 1 alphaSchizophrenia ATP5B, ATP5H, ATP6V1B, DNM1 Peripheral Neuropathic PainOX42, ED9 Chronic Neuropathic Pain chemokine receptor (CCR2/4) PrionDisease PrPSc, 14-3-3 zeta, S-100, AQP4 Stroke S-100, neuron specificenolase, PARK7, NDKA, ApoC-I, ApoC-III, SAA or AT-III fragment, Lp-PLA2, hs-CRP Cardiovascular Disease FATP6 Esophageal Cancer CaSRTuberculosis antigen 60, HSP, Lipoarabinomannan, Sulfolipid, antigen ofacylated trehalose family, DAT, TAT, Trehalose 6,6-dimycolate(cord-factor) antigen HIV gp41, gp120 Autism VIP, PACAP, CGRP, NT3Asthma YKL-40, S-nitrosothiols, SSCA2, PAI, amphiregulin, periostinLupus TNFR Cirrhosis NLT, HBsAg Influenza hemagglutinin, neurominidaseVulnerable Plaque Alpha v. Beta 3 integrin, MMP9

A cell-of-origin specific vesicle may be isolated using novel bindingagents, using methods as described herein. Furthermore, a cell-of-originspecific vesicle can also be isolated from a biological sample usingisolation methods based on cellular binding partners or binding agentsof such vesicles. Such cellular binding partners can include but are notlimited to peptides, proteins, RNA, DNA, apatmers, cells orserum-associated proteins that only bind to such vesicles when one ormore specific biomarkers are present. Isolation or deteciton of acell-of-origin specific vesicle can be carried out with a single bindingpartner or binding agent, or a combination of binding partners orbinding agents whose singular application or combined applicationresults in cell-of-origin specific isolation or detection. Non-limitingexamples of such binding agents are provided in FIG. 2. For example, avesicle for characterizing breast cancer can be isolated with one ormore binding agents including, but not limited to, estrogen,progesterone, Herceptin (Trastuzumab), CCND1, MYC PNA, IGF-1 PNA, MYCPNA, SC4 aptamer (Ku), AII-7 aptamer (ERB2), Galectin-3, mucin-typeO-glycans, L-PHA, Galectin-9, or any combination thereof.

A binding agent may also be used for isolating or detecting acell-of-origin specific vesicle based on: i) the presence of antigensspecific for cell-of-origin specific vesicles; ii) the absence ofmarkers specific for cell-of-origin specific vesicles; or iii)expression levels of biomarkers specific for cell-of-origin specificvesicles. A heterogeneous population of vesicles can be applied to asurface coated with specific binding agents designed to rule out oridentify the cell-of-origin characteristics of the vesicles. Variousbinding agents, such as antibodies, can be arrayed on a solid surface orsubstrate and the heterogeneous population of vesicles is allowed tocontact the solid surface or substrate for a sufficient time to allowinteractions to take place. Specific binding or non-binding to givenantibody locations on the array surface or substrate can then serve toidentify antigen specific characteristics of the vesicle population thatare specific to a given cell-of-origin.

A cell-of-origin specific vesicle can be enriched or isolated using oneor more binding agents using a magnetic capture method, fluorescenceactivated cell sorting (FACS) or laser cytometry as described above.Magnetic capture methods can include, but are not limited to, the use ofmagnetically activated cell sorter (MACS) microbeads or magneticcolumns. Examples of immunoaffinity and magnetic particle methods thatcan be used are described in U.S. Pat. No. 4,551,435, 4,795,698,4,925,788, 5,108,933, 5,186,827, 5,200,084 or 5,158,871. Acell-of-origin specific vesicle can also be isolated following thegeneral methods described in U.S. Pat. No. 7,399,632, by usingcombination of antigens specific to a vesicle.

Any other appropriate method for isolating or otherwise enriching thecell-of-origin specific vesicles with respect to a biological sample mayalso be used in combination with the present invention. For example,size exclusion chromatography such as gel permeation columns,centrifugation or density gradient centrifugation, and filtrationmethods can be used in combination with the antigen selection methodsdescribed herein. The cell-of-origin specific vesicles may also beisolated following the methods described in Koga et al., AnticancerResearch, 25:3703-3708 (2005), Taylor et al., Gynecologic Oncology,110:13-21 (2008), Nanjee et al., Clin Chem, 2000; 46:207-223 or U.S.Pat. No. 7,232,653.

Accordingly, vesicles can be isolated that are isolated from cellsderived from a tumor, or site of autoimmune disease, cardiovasculardisease, neurological disease, infection or other disease or disorder.In some embodiments, the isolated vesicles are derived from cellsrelated to such diseases and disorders, e.g., immune cells that play arole in the etiology of the disease and whose analysis is informativefor a diagnosis, prognosis, disease stratification, theranosis,prediction of responder/non-responder status, disease monitoring,treatment monitoring and the like as relates to such diseases anddisorders. The vesicles are further useful to discover biomarkers. Theisolated vesicles can then be assessed for characterizing a phenotype asdescribed herein.

Vesicle Assessment

A phenotype can be characterized for a subject by analyzing a biologicalsample from the subject and determining the level, amount, orconcentration of one or more populations of vesicles in the sample. Avesicle can be purified or concentrated prior to determining the amountof vesicles. Alternatively, the amount of vesicles can be directlyassayed from a sample, without prior purification or concentration. Thevesicles can be cell-of-origin specific vesicles or vesicles with aspecific biosignature. The amount of vesicles can be used whencharacterizing a phenotype, such as a diagnosis, prognosis, theranosis,or prediction of responder/non-responder status. In some embodiments,the amount is used to determine a physiological or biological state,such as pregnancy or the stage of pregnancy. The amount of vesicles canalso be used to determine treatment efficacy, stage of a disease orcondition, or progression of a disease or condition. For example, theamount of vesicles can be proportional or inversely proportional to anincrease in disease stage or progression. The amount of vesicles canalso be used to monitor progression of a disease or condition or tomonitor a subject's response to a treatment.

The vesicles can be evaluated by comparing the level of vesicles with areference level or value of vesicles. The reference value can beparticular to physical or temporal endpoint. For example, the referencevalue can be from the same subject from whom a sample is assessed, orthe reference value can be from a representative population of samples(e.g., samples from normal subjects not exhibiting a symptom ofdisease). Therefore, a reference value can provide a thresholdmeasurement which is compared to a subject sample's readout for avesicle population assayed in a given sample. Such reference values maybe set according to data pooled from groups of sample corresponding to aparticular cohort, including but not limited to age (e.g., newborns,infants, adolescents, young, middle-aged adults, seniors and adults ofvaried ages), racial/ethnic groups, normal versus diseased subjects,smoker v. non-smoker, subject receiving therapy versus untreatedsubject, different time points of treatment for a particular individualor group of subjects similarly diagnosed or treated or combinationsthereof. Furthermore, by determining vesicle levels at differenttimepoints of treatment for a particular individual, the individual'sresponse to the treatment or progression of a disease or condition forwhich the individual is being treated for, can be monitored.

A reference value may be based on samples assessed from the same subjectso to provide individualized tracking. Frequent testing of a patient mayprovide better comparisons to the reference values previouslyestablished for a particular patient and would allow a physician to moreaccurately assess the patient's disease stage or progression, and toinform a better decision for treatment. The reduced intraindividualvariance of vesicle levels can allow a more specific and individualizedthreshold to be defined for the patient. Temporal intrasubject variationallows each individual to serve as a longitudinal control for optimumanalysis of disease or physiological state.

Reference values can be established for unaffected individuals (ofvarying ages, ethnic backgrounds and sexes) without a particularphenotype by determining the amount of vesicles in an unaffectedindividual. For example, a reference value for a reference populationcan be used as a baseline for detection of one or more vesiclepopulations in a test subject. If a sample from a subject has a level orvalue that is similar to the reference, the subject can be identified tonot have the disease, or of having a low likelihood of developing adisease.

Alternatively, reference values or levels can be established forindividuals with a particular phenotype by determining the amount of oneor more populations of vesicles in an individual with the phenotype. Inaddition, an index of values can be generated for a particularphenotype. For example, different disease stages can have differentvalues, such as obtained from individuals with the different diseasestages. A subject's value can be compared to the index and a diagnosisor prognosis of the disease can be determined, such as the disease stageor progression. In other embodiments, an index of values is generatedfor therapeutic efficacies. For example, the level of vesicles ofindividuals with a particular disease can be generated and noted whattreatments were effective for the individual. The levels can be used togenerate values of which is a subject's value is compared, and atreatment or therapy can be selected for the individual, e.g., bypredicting from the levels whether the subject is likely to be aresponder or non-responder for a treatment.

In some embodiments, a reference value is determined for individualsunaffected with a particular cancer, by isolating or detecting vesicleswith an antigen that specifically targets biomarkers for the particularcancer. As a non-limiting example, individuals with varying stages ofcolorectal cancer and noncancerous polyps can be surveyed using the sametechniques described for unaffected individuals and the levels ofcirculating vesicles for each group can be determined. In someembodiments, the levels are defined as means±standard deviations from atleast two separate experiments performed in at least triplicate.Comparisons between these groups can be made using statistical tests todetermine statistical significance of distinguishing biomarkersobserved. In some embodiments, statistical significance is determinedusing a parametric statistical test. The parametric statistical test cancomprise, without limitation, a fractional factorial design, analysis ofvariance (ANOVA), a t-test, least squares, a Pearson correlation, simplelinear regression, nonlinear regression, multiple linear regression, ormultiple nonlinear regression. Alternatively, the parametric statisticaltest can comprise a one-way analysis of variance, two-way analysis ofvariance, or repeated measures analysis of variance. In otherembodiments, statistical significance is determined using anonparametric statistical test. Examples include, but are not limitedto, a Wilcoxon signed-rank test, a Mann-Whitney test, a Kruskal-Wallistest, a Friedman test, a Spearman ranked order correlation coefficient,a Kendall Tau analysis, and a nonparametric regression test. In someembodiments, statistical significance is determined at a p-value of lessthan 0.05, 0.01, 0.005, 0.001, 0.0005, or 0.0001. The p-values can alsobe corrected for multiple comparisons, e.g., using a Bonferronicorrection, a modification thereof, or other technique known to those inthe art, e.g., the Hochberg correction, Holm-Bonferroni correction,{hacek over (S)}idák correction, Dunnett's correction or Tukey'smultiple comparisons. In some embodiments, an ANOVA is followed byTukey's correction for post-test comparing of the biomarkers from eachpopulation.

Reference values can also be established for disease recurrencemonitoring (or exacerbation phase in MS), for therapeutic responsemonitoring, or for predicting responder/non-responder status.

In some embodiments, a reference value for microRNA obtained fromvesicles is determined using an artificial vesicle, also referred toherein as a synthetic vesicle. Methods for manufacturing artificialvesicles are known to those of skill in the art, e.g., using liposomes.Artificial vesicles can be manufactured using methods disclosed inUS20060222654 and U.S. Pat. No. 4,448,765, which are incorporated hereinby reference in its entirety. Artificial vesicles can be constructedwith known markers to facilitate capture and/or detection. In someembodiments, artificial vesicles are spiked into a bodily sample priorto processing. The level of intact synthetic vesicle can be trackedduring processing, e.g., using filtration or other isolation methodsdisclosed herein, to provide a control for the amount of vesicles in theinitial versus processed sample. Similarly, artificial vesicles can bespiked into a sample before or after any processing steps. In someembodiments, artificial vesicles are used to calibrate equipment usedfor isolation and detection of vesicles.

Artificial vesicles can be produced and used a control to test theviability of an assay, such as a bead-based assay. The artificialvesicle can bind to both the beads and to the detection antibodies.Thus, the artificial vesicle contains the amino acidsequence/conformation that each of the antibodies binds. The artificialvesicle can comprise a purified protein or a synthetic peptide sequenceto which the antibody binds. The artificial vesicle could be a bead,e.g., a polystyrene bead, that is capable of having biological moleculesattached thereto. If the bead has an available carboxyl group, then theprotein or peptide could be attached to the bead via an available aminegroup, such as using carbodiimide coupling.

In another embodiment, the artificial vesicle can be a polystyrene beadcoated with avidin and a biotin is placed on the protein or peptide ofchoice either at the time of synthesis or via a biotin-maleimidechemistry. The proteins/peptides to be on the bead can be mixed togetherin ratio specific to the application the artificial vesicle is beingused for, and then conjugated to the bead. These artificial vesicles canthen serve as a link between the capture beads and the detectionantibodies, thereby providing a control to show that the components ofthe assay are working properly.

The value can be a quantitative or qualitative value. The value can be adirect measurement of the level of vesicles (example, mass per volume),or an indirect measure, such as the amount of a specific biomarker. Thevalue can be a quantitative, such as a numerical value. In otherembodiments, the value is qualitiative, such as no vesicles, low levelof vesicles, medium level, high level of vesicles, or variationsthereof.

The reference value can be stored in a database and used as a referencefor the diagnosis, prognosis, theranosis, disease stratification,disease monitoring, treatment monitoring or prediction ofnon-responder/responder status of a disease or condition based on thelevel or amount of microRNA, such as total amount of microRNA, or theamount of a specific population of microRNA, such as cell-of-originspecific microRNA or microRNA from vesicles with a specificbiosignature. In an illustrative example, consider a method ofdetermining a diagnosis for a cancer. MicroRNA from reference subjectswith and without the cancer are assessed and stored in the database. Thereference subjects provide biosignature indicative of the cancer or ofanother state, e.g., a healthy state. A sample from a test subject isthen assayed and the microRNA biosignature is compared against those inthe database. If the subject's biosignature correlates more closely withreference values indicative of cancer, a diagnosis of cancer may bemade. Conversely, if the subject's biosignature correlates more closelywith reference values indicative of a healthy state, the subject may bedetermined to not have the disease. One of skill will appreciate thatthis example is non-limiting and can be expanded for assessing otherphenotypes, e.g., other diseases, prognosis, theranosis, diseasestratification, disease monitoring, treatment monitoring or predictionof non-responder/responder status, and the like.

A biosignature for characterizing a phenotype can be determined bydetecting microRNA and/or vesicles. The microRNA can be assessed withina vesicle. Alternately, the microRNA and vesicles in a sample areanalyzed to characterize the phenotype without isolating the microRNAfrom the vesicles. Many analytical techniques are available to assessvesicles. In some embodiments, vesicle levels are characterized usingmass spectrometry, flow cytometry, immunocytochemical staining, Westernblotting, electrophoresis, chromatography or x-ray crystallography inaccordance with procedures known in the art. For example, vesicles canbe characterized and quantitatively measured using flow cytometry asdescribed in Clayton et al., Journal of Immunological Methods 2001;163-174, which is herein incorporated by reference in its entirety.Vesicle levels may be determined using binding agents as describedabove. For example, a binding agent to vesicles can be labeled and thelabel detected and used to determine the amount of vesicles in a sample.The binding agent can be bound to a substrate, such as arrays orparticles, such as described above. Alternatively, the vesicles may belabeled directly.

Electrophoretic tags or eTags can be used to determine the amount ofvesicles. eTags are small fluorescent molecules linked to nucleic acidsor antibodies and are designed to bind one specific nucleic acidsequence or protein, respectively. After the eTag binds its target, anenzyme is used to cleave the bound eTag from the target. The signalgenerated from the released eTag, called a “reporter,” is proportionalto the amount of target nucleic acid or protein in the sample. The eTagreporters can be identified by capillary electrophoresis. The uniquecharge-to-mass ratio of each eTag reporter—that is, its electricalcharge divided by its molecular weight—makes it show up as a specificpeak on the capillary electrophoresis readout. Thus by targeting aspecific biomarker of a vesicle with an eTag, the amount or level ofvesicles can be determined

The vesicle level can determined from a heterogeneous population ofvesicles, such as the total population of vesicles in a sample.Alternatively, the vesicles level is determined from a homogenouspopulation, or substantially homogenous population of vesicles, such asthe level of specific cell-of-origin vesicles, such as vesicles fromprostate cancer cells. In yet other embodiments, the level is determinedfor vesicles with a particular biomarker or combination of biomarkers,such as a biomarker specific for prostate cancer. Determining the levelvesicles can be performed in conjunction with determining the biomarkeror combination of biomarkers of a vesicle. Alternatively, determiningthe amount of vesicle may be performed prior to or subsequent todetermining the biomarker or combination of biomarkers of the vesicles.

Determining the amount of vesicles can be assayed in a multiplexedmanner. For example, determining the amount of more than one populationof vesicles, such as different cell-of-origin specific vesicles withdifferent biomarkers or combination of biomarkers, can be performed,such as those disclosed herein.

Performance of a diagnostic or related test is typically assessed usingstatistical measures. The performance of the characterization can beassessed by measuring sensitivity, specificity and related measures. Forexample, a level of microRNAs of interest can be assayed to characterizea phenotype, such as detecting a disease. The sensitivity andspecificity of the assay to detect the disease is determined.

A true positive is a subject with a characteristic, e.g., a disease ordisorder, correctly identified as having the characteristic. A falsepositive is a subject without the characteristic that the testimproperly identifies as having the characteristic. A true negative is asubject without the characteristic that the test correctly identifies asnot having the characteristic. A false negative is a person with thecharacteristic that the test improperly identifies as not having thecharacteristic. The ability of the test to distinguish between theseclasses provides a measure of test performance.

The specificity of a test is defined as the number of true negativesdivided by the number of actual negatives (i.e., sum of true negativesand false positives). Specificity is a measure of how many subjects arecorrectly identified as negatives. A specificity of 100% means that thetest recognizes all actual negatives—for example, all healthy peoplewill be recognized as healthy. A lower specificity indicates that morenegatives will be determined as positive.

The sensitivity of a test is defined as the number of true positivesdivided by the number of actual positives (i.e., sum of true positivesand false negatives). Specificity is a measure of how many subjects arecorrectly identified as positives. A sensitivity of 100% means that thetest recognizes all actual positives—for example, all sick people willbe recognized as sick. A lower sensitivity indicates that more positiveswill be missed by being determined as negative.

The accuracy of a test is defined as the number of true positives andtrue negatives divided by the sum of all true and false positives andall true and false negatives. It provides one number that combinessensitivity and specificity measurements.

Sensitivity, specificity and accuracy are determined at a particulardiscrimination threshold value. For example, a common threshold forprostate cancer (PCa) detection is 4 ng/mL of prostate specific antigen(PSA) in serum. A level of PSA equal to or above the threshold isconsidered positive for PCa and any level below is considered negative.As the threshold is varied, the sensitivity and specificity will alsovary. For example, as the threshold for detecting cancer is increased,the specificity will increase because it is harder to call a subjectpositive, resulting in fewer false positives. At the same time, thesensitivity will decrease. A receiver operating characteristic curve(ROC curve) is a graphical plot of the true positive rate (i.e.,sensitivity) versus the false positive rate (i.e., 1−specificity) for abinary classifier system as its discrimination threshold is varied. TheROC curve shows how sensitivity and specificity change as the thresholdis varied. The Area Under the Curve (AUC) of an ROC curve provides asummary value indicative of a test's performance over the entire rangeof thresholds. The AUC is equal to the probability that a classifierwill rank a randomly chosen positive sample higher than a randomlychosen negative sample. An AUC of 0.5 indicates that the test has a 50%chance of proper ranking, which is equivalent to no discriminatory power(a coin flip also has a 50% chance of proper ranking). An AUC of 1.0means that the test properly ranks (classifies) all subjects. The AUC isequivalent to the Wilcoxon test of ranks.

A biosignature according to the invention can be used to characterize aphenotype with at least 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,62, 63, 64, 65, 66, 67, 68, 69, or 70% sensitivity, such as with atleast 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, or87% sensitivity. In some embodiments, the phenotype is characterizedwith at least 87.1, 87.2, 87.3, 87.4, 87.5, 87.6, 87.7, 87.8, 87.9,88.0, or 89% sensitivity, such as at least 90% sensitivity. Thephenotype can be characterized with at least 91, 92, 93, 94, 95, 96, 97,98, 99 or 100% sensitivity.

A biosignature according to the invention can be used to characterize aphenotype of a subject with at least 50, 51, 52, 53, 54, 55, 56, 57, 58,59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76,77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94,95, 96, or 97% specificity, such as with at least 97.1, 97.2, 97.3,97.4, 97.5, 97.6, 97.7, 97.8, 97.8, 97.9, 98.0, 98.1, 98.2, 98.3, 98.4,98.5, 98.6, 98.7, 98.8, 98.9, 99.0, 99.1, 99.2, 99.3, 99.4, 99.5, 99.6,99.7, 99.8, 99.9 or 100% specificity.

A biosignature according to the invention can be used to characterize aphenotype of a subject, e.g., based on microRNA level or othercharacteristic, with at least 50% sensitivity and at least 60, 65, 70,75, 80, 85, 90, 95, 99, or 100% specificity; at least 55% sensitivityand at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; atleast 60% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99,or 100% specificity; at least 65% sensitivity and at least 60, 65, 70,75, 80, 85, 90, 95, 99, or 100% specificity; at least 70% sensitivityand at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; atleast 75% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99,or 100% specificity; at least 80% sensitivity and at least 60, 65, 70,75, 80, 85, 90, 95, 99, or 100% specificity; at least 85% sensitivityand at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; atleast 86% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99,or 100% specificity; at least 87% sensitivity and at least 60, 65, 70,75, 80, 85, 90, 95, 99, or 100% specificity; at least 88% sensitivityand at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; atleast 89% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99,or 100% specificity; at least 90% sensitivity and at least 60, 65, 70,75, 80, 85, 90, 95, 99, or 100% specificity; at least 91% sensitivityand at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; atleast 92% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99,or 100% specificity; at least 93% sensitivity and at least 60, 65, 70,75, 80, 85, 90, 95, 99, or 100% specificity; at least 94% sensitivityand at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; atleast 95% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99,or 100% specificity; at least 96% sensitivity and at least 60, 65, 70,75, 80, 85, 90, 95, 99, or 100% specificity; at least 97% sensitivityand at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; atleast 98% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99,or 100% specificity; at least 99% sensitivity and at least 60, 65, 70,75, 80, 85, 90, 95, 99, or 100% specificity; or substantially 100%sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100%specificity.

A biosignature according to the invention can be used to characterize aphenotype of a subject with at least 60, 61, 62, 63, 64, 65, 66, 67, 68,69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86,87, 88, 89, 90, 91, 92, 93, 94, 95, 96, or 97% accuracy, such as with atleast 97.1, 97.2, 97.3, 97.4, 97.5, 97.6, 97.7, 97.8, 97.8, 97.9, 98.0,98.1, 98.2, 98.3, 98.4, 98.5, 98.6, 98.7, 98.8, 98.9, 99.0, 99.1, 99.2,99.3, 99.4, 99.5, 99.6, 99.7, 99.8, 99.9 or 100% accuracy.

In some embodiments, a biosignature according to the invention is usedto characterize a phenotype of a subject with an AUC of at least 0.60,0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72,0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84,0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96,or 0.97, such as with at least 0.971, 0.972, 0.973, 0.974, 0.975, 0.976,0.977, 0.978, 0.978, 0.979, 0.980, 0.981, 0.982, 0.983, 0.984, 0.985,0.986, 0.987, 0.988, 0.989, 0.99, 0.991, 0.992, 0.993, 0.994, 0.995,0.996, 0.997, 0.998, 0.999 or 1.00.

Furthermore, the confidence level for determining the specificity,sensitivity, accuracy or AUC, may be determined with at least 50, 51,52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99% confidence.

Other related performance measures include positive and negativelikelihood ratios [positive LR=sensitivity/(1−specificity); negativeLR=(1−sensitivity)/specificity]. Such measures can also be used to gaugetest performance according to the methods of the invention.

Classification

Biosignature according to the invention can be used to classify asample. Techniques for discriminate analysis are known to those of skillin the art. For example, a sample can be classified as, or predicted tobe, a responder or non-responder to a given treatment for a givendisease or disorder. Many statistical classification techniques areknown to those of skill in the art. In supervised learning approaches, agroup of samples from two or more groups are analyzed with a statisticalclassification method. Biomarkers can be discovered that can be used tobuild a classifier that differentiates between the two or more groups. Anew sample can then be analyzed so that the classifier can associate thenew with one of the two or more groups. Commonly used supervisedclassifiers include without limitation the neural network (multi-layerperceptron), support vector machines, k-nearest neighbors, Gaussianmixture model, Gaussian, naive Bayes, decision tree and radial basisfunction (RBF) classifiers. Linear classification methods includeFisher's linear discriminant, logistic regression, naive Bayesclassifier, perceptron, and support vector machines (SVMs). Otherclassifiers for use with the invention include quadratic classifiers,k-nearest neighbor, boosting, decision trees, random forests, neuralnetworks, pattern recognition, Bayesian networks and Hidden Markovmodels. One of skill will appreciate that these or other classifiers,including improvements of any of these, are contemplated within thescope of the invention.

Classification using supervised methods is generally performed by thefollowing methodology:

In order to solve a given problem of supervised learning (e.g. learningto recognize handwriting) one has to consider various steps:

1. Gather a training set. These can include, for example, samples thatare from a subject with or without a disease or disorder, subjects thatare known to respond or not respond to a treatment, subjects whosedisease progresses or does not progress, etc. The training samples areused to “train” the classifier.

2. Determine the input “feature” representation of the learned function.The accuracy of the learned function depends on how the input object isrepresented. Typically, the input object is transformed into a featurevector, which contains a number of features that are descriptive of theobject. The number of features should not be too large, because of thecurse of dimensionality; but should be large enough to accuratelypredict the output. The features might include a set of biomarkers suchas those derived from vesicles as described herein.

3. Determine the structure of the learned function and correspondinglearning algorithm. A learning algorithm is chosen, e.g., artificialneural networks, decision trees, Bayes classifiers or support vectormachines. The learning algorithm is used to build the classifier.

4. Build the classifier. The learning algorithm is run the gatheredtraining set. Parameters of the learning algorithm may be adjusted byoptimizing performance on a subset (called a validation set) of thetraining set, or via cross-validation. After parameter adjustment andlearning, the performance of the algorithm may be measured on a test setof naive samples that is separate from the training set.

Once the classifier is determined as described above, it can be used toclassify a sample, e.g., that of a subject who is being analyzed by themethods of the invention. As an example, a classifier can be built usingdata for levels of microRNA of interest in reference subjects with andwithout a disease as the training and test sets. MicroRNA levels foundin a sample from a test subject are assessed and the classifier is usedto classify the subject as with or without the disease. As anotherexample, a classifier can be built using data for levels of vesiclebiomarkers of interest in reference subjects that have been found torespond or not respond to certain diseases as the training and testsets. The vesicle biomarker levels found in a sample from a test subjectare assessed and the classifier is used to classify the subject as withor without the disease.

Unsupervised learning approaches can also be used with the invention.Clustering is an unsupervised learning approach wherein a clusteringalgorithm correlates a series of samples without the use the labels. Themost similar samples are sorted into “clusters.” A new sample could besorted into a cluster and thereby classified with other members that itmost closely associates. Many clustering algorithms well known to thoseof skill in the art can be used with the invention, such as hierarchicalclustering.

Biosignatures

A biosignature can be obtained according to the invention by assessing avesicle population, including surface and payload vesicle associatedbiomarkers, and/or circulating biomarkers including microRNA andprotein. A biosignature derived from a subject can be used tocharacterize a phenotype of the subject. A biosignature can furtherinclude the level of one or more additional biomarkers, e.g.,circulating biomarkers or biomarkers associated with a vesicle ofinterest. A biosignature of a vesicle of interest can include particularantigens or biomarkers that are present on the vesicle. The biosignaturecan also include one or more antigens or biomarkers that are carried aspayload within the vesicle, including the microRNA under examination.The biosignature can comprise a combination of one or more antigens orbiomarkers that are present on the vesicle with one or more biomarkersthat are detected in the vesicle. The biosignature can further compriseother information about a vesicle aside from its biomarkers. Suchinformation can include vesicle size, circulating half-life, metabolichalf-life, and specific activity in vivo or in vitro. The biosignaturecan comprise the biomarkers or other characteristics used to build aclassifier.

In some embodiments, the microRNA is detected directly in a biologicalsample. For example, RNA in a bodily fluid can be isolated usingcommercially available kits such as mirVana kits (AppliedBiosystems/Ambion, Austin, Tex.), MagMAX™ RNA Isolation Kit (AppliedBiosystems/Ambion, Austin, Tex.), and QIAzol Lysis Reagent and RNeasyMidi Kit (Qiagen Inc., Valencia Calif.). Particular species of microRNAscan be determined using array or PCR techniques as described below.

In some embodiments, the microRNA payload with vesicles is assessed inorder to characterize a phenotype. The vesicles can be purified orconcentrated prior to determining the biosignature. For example, acell-of-origin specific vesicle can be isolated and its biosignaturedetermined. Alternatively, the biosignature of the vesicle can bedirectly assayed from a sample, without prior purification orconcentration. The biosignature of the invention can be used todetermine a diagnosis, prognosis, or theranosis of a disease orcondition or similar measures described herein. A biosignature can alsobe used to determine treatment efficacy, stage of a disease orcondition, or progression of a disease or condition, orresponder/non-responder status. Furthermore, a biosignature may be usedto determine a physiological state, such as pregnancy.

A characteristic of a vesicle in and of itself can be assessed todetermine a biosignature. The characteristic can be used to diagnose,detect or determine a disease stage or progression, the therapeuticimplications of a disease or condition, or characterize a physiologicalstate. Such characteristics include without limitation the level oramount of vesicles, vesicle size, temporal evaluation of the variationin vesicle half-life, circulating vesicle half-life, metabolic half-lifeof a vesicle, or activity of a vesicle.

Biomarkers that can be included in a biosignature include one or moreproteins or peptides (e.g., providing a protein signature), nucleicacids (e.g. RNA signature as described, or a DNA signature), lipids(e.g. lipid signature), or combinations thereof. In some embodiments,the biosignature can also comprise the type or amount of drug or drugmetabolite present in a vesicle, (e.g., providing a drug signature), assuch drug may be taken by a subject from which the biological sample isobtained, resulting in a vesicle carrying the drug or metabolites of thedrug.

A biosignature can also include an expression level, presence, absence,mutation, variant, copy number variation, truncation, duplication,modification, or molecular association of one or more biomarkers. Agenetic variant, or nucleotide variant, refers to changes or alterationsto a gene or cDNA sequence at a particular locus, including, but notlimited to, nucleotide base deletions, insertions, inversions, andsubstitutions in the coding and non-coding regions. Deletions may be ofa single nucleotide base, a portion or a region of the nucleotidesequence of the gene, or of the entire gene sequence. Insertions may beof one or more nucleotide bases. The genetic variant may occur intranscriptional regulatory regions, untranslated regions of mRNA, exons,introns, or exon/intron junctions. The genetic variant may or may notresult in stop codons, frame shifts, deletions of amino acids, alteredgene transcript splice forms or altered amino acid sequence.

In an embodiment, nucleic acid biomarkers, including nucleic acidpayload within a vesicle, is assessed for nucleotide variants. Thenucleic acid biomarker may comprise one or more RNA species, e.g., mRNA,miRNA, snoRNA, snRNA, rRNAs, tRNAs, siRNA, hnRNA, shRNA, or acombination thereof. Similarly, DNA payload can be assessed to form aDNA signature.

An RNA signature or DNA signature can also include a mutational,epigenetic modification, or genetic variant analysis of the RNA or DNApresent in the vesicle. Epigenetic modifications include patterns of DNAmethylation. See, e.g., Lesche R. and Eckhardt F., DNA methylationmarkers: a versatile diagnostic tool for routine clinical use. Curr OpinMol Ther. 2007 June; 9(3):222-30, which is incorporated herein byreference in its entirety. Thus, a biomarker can be the methylationstatus of a segment of DNA.

A biosignature can comprise one or more miRNA signatures combined withone or more additional signatures including, but not limited to, an mRNAsignature, DNA signature, protein signature, peptide signature, antigensignature, or any combination thereof. For example, the biosignature cancomprise one or more miRNA biomarkers with one or more DNA biomarkers,one or more mRNA biomarkers, one or more snoRNA biomarkers, one or moreprotein biomarkers, one or more peptide biomarkers, one or more antigenbiomarkers, one or more antigen biomarkers, one or more lipidbiomarkers, or any combination thereof.

A biosignature can comprise a combination of one or more antigens orbinding agents (such as ability to bind one or more binding agents),such as listed in FIGS. 1 and 2, respectively, or those describedelsewhere herein. The biosignature can further comprise one or moreother biomarkers, such as, but not limited to, miRNA, DNA (e.g. singlestranded DNA, complementary DNA, or noncoding DNA), or mRNA. Thebiosignature of a vesicle can comprise a combination of one or moreantigens, such as shown in FIG. 1, one or more binding agents, such asshown in FIG. 2, and one or more biomarkers for a condition or disease,such as listed in FIGS. 3-60. The biosignature can comprise one or morebiomarkers, for example miRNA, with one or more antigens specific for acancer cell (for example, as shown in FIG. 1).

In some embodiments, a vesicle used in the subject methods has abiosignature that is specific to the cell-of-origin and is used toderive disease-specific or biological state specific diagnostic,prognostic or therapy-related biosignatures representative of thecell-of-origin. In other embodiments, a vesicle has a biosignature thatis specific to a given disease or physiological condition that isdifferent from the biosignature of the cell-of-origin for use in thediagnosis, prognosis, staging, therapy-related determinations orphysiological state characterization. Biosignatures can also comprise acombination of cell-of-origin specific and non-specific vesicles.

Biosignatures can be used to evaluate diagnostic criteria such aspresence of disease, disease staging, disease monitoring, diseasestratification, or surveillance for detection, metastasis or recurrenceor progression of disease. A biosignature can also be used clinically inmaking decisions concerning treatment modalities including therapeuticintervention. A biosignature can further be used clinically to maketreatment decisions, including whether to perform surgery or whattreatment standards should be utilized along with surgery (e.g., eitherpre-surgery or post-surgery). As an illustrative example, a microRNA(miRNA) biosignature that indicates an aggressive form of cancer maycall for a more aggressive surgical procedure and/or more aggressivetherapeutic regimen to treat the patient.

A biosignature can be used in therapy related diagnostics to providetests useful to diagnose a disease or choose the correct treatmentregimen, such as provide a theranosis. Theranostics includes diagnostictesting that provides the ability to affect therapy or treatment of adiseased state. Theranostics testing provides a theranosis in a similarmanner that diagnostics or prognostic testing provides a diagnosis orprognosis, respectively. As used herein, theranostics encompasses anydesired form of therapy related testing, including predictive medicine,personalized medicine, integrated medicine, pharmacodiagnostics andDx/Rx partnering. Therapy related tests can be used to predict andassess drug response in individual subjects, i.e., to providepersonalized medicine. Predicting a drug response can be determiningwhether a subject is a likely responder or a likely non-responder to acandidate therapeutic agent, e.g., before the subject has been exposedor otherwise treated with the treatment. Assessing a drug response canbe monitoring a response to a drug, e.g., monitoring the subject'simprovement or lack thereof over a time course after initiating thetreatment. Therapy related tests are useful to select a subject fortreatment who is particularly likely to benefit from the treatment or toprovide an early and objective indication of treatment efficacy in anindividual subject. Thus, a biosignature as disclosed herein mayindicate that treatment should be altered to select a more promisingtreatment, thereby avoiding the great expense of delaying beneficialtreatment and avoiding the financial and morbidity costs ofadministering an ineffective drug(s).

Therapy related diagnostics are also useful in clinical diagnosis andmanagement of a variety of diseases and disorders, which include, butare not limited to cardiovascular disease, cancer, infectious diseases,sepsis, neurological diseases, central nervous system related diseases,endovascular related diseases, and autoimmune related diseases. Therapyrelated diagnostics also aid in the prediction of drug toxicity, drugresistance or drug response. Therapy related tests may be developed inany suitable diagnostic testing format, which include, but are notlimited to, e.g., immunohistochemical tests, clinical chemistry,immunoassay, cell-based technologies, nucleic acid tests or body imagingmethods. Therapy related tests can further include but are not limitedto, testing that aids in the determination of therapy, testing thatmonitors for therapeutic toxicity, or response to therapy testing. Thus,a biosignature can be used to predict or monitor a subject's response toa treatment. A biosignature can be determined at different time pointsfor a subject after initiating, removing, or altering a particulartreatment.

In some embodiments, a determination or prediction as to whether asubject is responding to a treatment is made based on a change in theamount of one or more components of a biosignature (i.e., the microRNA,vesicles and/or biomarkers of interest), an amount of one or morecomponents of a particular biosignature, or the biosignature detectedfor the components. In another embodiment, a subject's condition ismonitored by determining a biosignature at different time points. Theprogression, regression, or recurrence of a condition is determined.Response to therapy can also be measured over a time course. Thus, theinvention provides a method of monitoring a status of a disease or othermedical condition in a subject, comprising isolating or detecting abiosignature from a biological sample from the subject, detecting theoverall amount of the components of a particular biosignature, ordetecting the biosignature of one or more components (such as thepresence, absence, or expression level of a biomarker). Thebiosignatures are used to monitor the status of the disease orcondition.

In some embodiments, a biosignature is used to determine whether aparticular disease or condition is resistant to a drug. If a subject isdrug resistant, a physician need not waste valuable time with such drugtreatment. To obtain early validation of a drug choice or treatmentregimen, a biosignature is determined for a sample obtained from asubject. The biosignature is used to assess whether the particularsubject's disease has the biomarker associated with drug resistance.Such a determination enables doctors to devote critical time as well asthe patient's financial resources to effective treatments.

Moreover, biosignature may be used to assess whether a subject isafflicted with disease, is at risk for developing disease or to assessthe stage or progression of the disease. For example, a biosignature canbe used to assess whether a subject has prostate cancer (for example,FIG. 68, 73) or colon cancer (for example, FIG. 69, 74). Furthermore, abiosignature can be used to determine a stage of a disease or condition,such as colon cancer (for example, FIGS. 71, 72).

Furthermore, determining the amount of vesicles, such a heterogeneouspopulation of vesicles, and the amount of one or more homogeneouspopulation of vesicles, such as a population of vesicles with the samebiosignature, can be used to characterize a phenotype. For example,determination of the total amount of vesicles in a sample (i.e. notcell-type specific) and determining the presence of one or moredifferent cell-of-origin specific vesicles can be used to characterize aphenotype. Threshold values, or reference values or amounts can bedetermined based on comparisons of normal subjects and subjects with thephenotype of interest, as further described below, and criteria based onthe threshold or reference values determined The different criteria canbe used to characterize a phenotype.

One criterion can be based on the amount of a heterogeneous populationof vesicles in a sample. In one embodiment, general vesicle markers,such as CD9, CD81, and CD63 can be used to determine the amount ofvesicles in a sample. The expression level of CD9, CD81, CD63, or acombination thereof can be detected and if the level is greater than athreshold level, the criterion is met. In another embodiment, thecriterion is met if if level of CD9, CD81, CD63, or a combinationthereof is lower than a threshold value or reference value. In anotherembodiment, the criterion can be based on whether the amount of vesiclesis higher than a threshold or reference value. Another criterion can bebased on the amount of vesicles with a specific biosignature. If theamount of vesicles with the specific biosignature is lower than athreshold or reference value, the criterion is met. In anotherembodiment, if the amount of vesicles with the specific biosignature ishigher than a threshold or reference value, the criterion is met. Acriterion can also be based on the amount of vesicles derived from aparticular cell type. If the amount is lower than a threshold orreference value, the criterion is met. In another embodiment, if theamount is higher than a threshold value, the criterion is met.

In a non-limiting example, consider that vesicles from prostate cellsare determined by detecting the biomarker PCSA or PSCA, and that acriterion is met if the level of detected PCSA or PSCA is greater than athreshold level. The threshold can be the level of the same markers in asample from a control cell line or control subject. Another criterioncan be based on whether the amount of vesicles derived from a cancercell or comprising one or more cancer specific biomarkers. For example,the biomarkers B7H3, EpCam, or both, can be determined and a criterionmet if the level of detected B7H3 and/or EpCam is greater than athreshold level or within a pre-determined range. If the amount islower, or higher, than a threshold or reference value, the criterion ismet. A criterion can also be the reliability of the result, such asmeeting a quality control measure or value. A detected amount of B7H3and/or EpCam in a test sample that is above the amount of these markersin a control sample may indicate the presence of a cancer in the testsample.

As described, analysis of multiple markers can be combined to assesswhether a criterion is met. In an illustrative example, a biosignatureis used to assess whether a subject has prostate cancer by detecting oneor more of the general vesicle markers CD9, CD63 and CD81; one or moreprostate epithelial markers including PCSA or PSMA; and one or morecancer markers such as B7H3 and/or EpCam. Higher levels of the markersin a sample from a subject than in a control individual without prostatecancer indicates the presence of the prostate cancer in the subject. Insome embodiments, the multiple markers are assessed in a multiplexfashion.

One of skill will understand that such rules based on meeting criterionas described can be applied to any appropriate biomarker. For example,the criterion can be applied to vesicle characteristics such as amountof vesicles present, amount of vesicles with a particular biosignaturepresent, amount of vesicle payload biomarkers present, amount ofmicroRNA or other circulating biomarkers present, and the like. Theratios of appropriate biomarkers can be determined. As illustrativeexamples, the criterion could be a ratio of an vesicle surface proteinto another vesicle surface protein, a ratio of an vesicle surfaceprotein to a microRNA, a ratio of one vesicle population to anothervesicle population, a ratio of one circulating biomarker to anothercirculating biomarker, etc.

A phenotype for a subject can be characterized based on meeting anynumber of useful criteria. In some embodiments, at least one criterionis used for each biomarker. In some embodiments, at least 1, 2, 3, 4, 5,6, 7, 8, 9, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90 or at least 100criteria are used. For example, for the characterizing of a cancer, anumber of different criteria can be used when the subject is diagnosedwith a cancer: 1) if the amount of microRNA in a sample from a subjectis higher than a reference value; 2) if the amount of a microRNA withincell type specific vesicles (i.e. vesicles derived from a specifictissue or organ) is higher than a reference value; or 3) if the amountof microRNA within vesicles with one or more cancer specific biomarkersis higher than a reference value. Similar rules can apply if the amountof microRNA is less than or the same as the reference. The method canfurther include a quality control measure, such that the results areprovided for the subject if the samples meet the quality controlmeasure. In some embodiments, if the criteria are met but the qualitycontrol is questionable, the subject is reassessed.

In other embodiments, a single measure is determined for assessment ofmultiple biomarkers, and the measure is compared to a reference. Forillustration, a test for prostate cancer might comprise multiplying thelevel of PSA against the level of miR-141 in a blood sample. Thecriterion is met if the product of the levels is above a threshold,indicating the presense of the cancer. As another illustration, a numberof binding agents to general vesicle markers can carry the same label,e.g., the same fluorophore. The level of the detected label can becompared to a threshold.

Criterion can be applied to multiple types of biomarkers in addition tomultiple biomarkers of the same type. For example, the levels of one ormore circulating biomarkers (e.g., RNA, DNA, peptides), vesicles,mutations, etc, can be compared to a reference. Different components ofa biosignature can have different criteria. As a non-limiting example, abiosignature used to diagnose a cancer can include overexpression of onemiR species as compared to a reference and underexpression of a vesiclesurface antigen as compared to another reference.

A biosignature can be determined by comparing the amount of vesicles,the structure of a vesicle, or any other informative characteristic of avesicle. Vesicle structure can be assessed using transmission electronmicroscopy, see for example, Hansen et al., Journal of Biomechanics 31,Supplement 1: 134-134(1) (1998), or scanning electron microscopy.Various combinations of methods and techniques or analyzing one or morevesicles can be used to determine a phenotype for a subject.

A biosignature can include without limitation the presence or absence,copy number, expression level, or activity level of a biomarker. Otheruseful components of a biosignature include the presence of a mutation(e.g., mutations which affect activity of a transcription or translationproduct, such as substitution, deletion, or insertion mutations),variant, or post-translation modification of a biomarker.Post-translational modification of a protein biomarker include withoutlimitation acylation, acetylation, phosphorylation, ubiquitination,deacetylation, alkylation, methylation, amidation, biotinylation,gamma-carboxylation, glutamylation, glycosylation, glycyation,hydroxylation, covalent attachment of heme moiety, iodination,isoprenylation, lipoylation, prenylation, GPI anchor formation,myristoylation, farnesylation, geranylgeranylation, covalent attachmentof nucleotides or derivatives thereof, ADP-ribosylation, flavinattachment, oxidation, palmitoylation, pegylation, covalent attachmentof phosphatidylinositol, phosphopantetheinylation, polysialylation,pyroglutamate formation, racemization of proline by prolyl isomerase,tRNA-mediation addition of amino acids such as arginylation, sulfation,the addition of a sulfate group to a tyrosine, or selenoylation of thebiomarker.

The methods described herein can be used to identify a biosignature thatis associated with a disease, condition or physiological state. Thebiosignature can also be utilized to determine if a subject is afflictedwith cancer or is at risk for developing cancer. A subject at risk ofdeveloping cancer can include those who may be predisposed or who havepre-symptomatic early stage disease.

A biosignature can also be utilized to provide a diagnostic ortheranostic determination for other diseases including but not limitedto autoimmune diseases, inflammatory bowel diseases, Alzheimer'sdisease, Parkinson's disease, Multiple Sclerosis, sepsis or pancreatitisor any disease, conditions or symptoms listed in FIGS. 3-58.

The biosignature can also be used to identify a given pregnancy statefrom the peripheral blood, umbilical cord blood, or amniotic fluid (e.g.miRNA signature specific to Downs Syndrome) or adverse pregnancy outcomesuch as pre-eclampsia, pre-term birth, premature rupture of membranes,intrauterine growth restriction or recurrent pregnancy loss. Thebiosignature can also be used to indicate the health of the mother, thefetus at all developmental stages, the pre-implantation embryo or anewborn.

A biosignature can be utilized for pre-symptomatic diagnosis.Furthermore, the biosignature can be utilized to detect disease,determine disease stage or progression, determine the recurrence ofdisease, identify treatment protocols, determine efficacy of treatmentprotocols or evaluate the physiological status of individuals related toage and environmental exposure.

Monitoring a biosignature of a vesicle can also be used to identifytoxic exposures in a subject including, but not limited to, situationsof early exposure or exposure to an unknown or unidentified toxic agent.Without being bound by any one specific theory for mechanism of action,vesicles can shed from damaged cells and in the process compartmentalizespecific contents of the cell including both membrane components andengulfed cytoplasmic contents. Cells exposed to toxic agents/chemicalsmay increase vesicle shedding to expel toxic agents or metabolitesthereof, thus resulting in increased vesicle levels. Thus, monitoringvesicle levels, vesicle biosignature, or both, allows assessment of anindividual's response to potential toxic agent(s).

A vesicle and/or other biomarkers of the invention can be used toidentify states of drug-induced toxicity or the organ injured, bydetecting one or more specific antigen, binding agent, biomarker, or anycombination thereof. The level of vesicles, changes in the biosignatureof a vesicle, or both, can be used to monitor an individual for acute,chronic, or occupational exposures to any number of toxic agentsincluding, but not limited to, drugs, antibiotics, industrial chemicals,toxic antibiotic metabolites, herbs, household chemicals, and chemicalsproduced by other organisms, either naturally occurring or synthetic innature. In addition, a biosignature can be used to identify conditionsor diseases, including cancers of unknown origin, also known as cancersof unknown primary (CUP).

A vesicle may be isolated from a biological sample as previouslydescribed to arrive at a heterogeneous population of vesicles. Theheterogeneous population of vesicles can then be contacted withsubstrates coated with specific binding agents designed to rule out oridentify antigen specific characteristics of the vesicle population thatare specific to a given cell-of-origin. Further, as described above, thebiosignature of a vesicle can correlate with the cancerous state ofcells. Compounds that inhibit cancer in a subject may cause a change,e.g., a change in biosignature of a vesicle, which can be monitored byserial isolation of vesicles over time and treatment course. The levelof vesicles or changes in the level of vesicles with a specificbiosignature can be monitored.

In one aspect, the present invention relates to biomarker discovery andbiosignature discovery. In an embodiment, one or more subjects thatrespond to a therapy (responders) and one or more subjects that do notrespond to the same therapy (non-responders) can have their vesiclesinterrogated. Interrogation can be performed to identify the presence ofone or more biomarkers, including any of the biomarkers describedherein. In one aspect, the presence, quantity, and payload of a miR areassayed. The payload of a miR can be, for example, and surface orinternal protein, nucleic acid, lipid or carbohydrate.

The presence or absence of a biosignature in responders but not in thenon-responders can be used for theranosis. A sample from responders maybe analyzed for one or more of the following: amount of vesicles, amountof a unique subset or species of vesicles, biomarkers in such vesicles,biosignature of such vesicles, etc. In one instance, vesicles such asmicrovesicles or exosomes from responders and non-responders areanalyzed for the presence and/or quantity of one or more miRNAs, such asmiRNA 122, miR-548c-5p, miR-362-3p, miR-422a, miR-597, miR-429,miR-200a, and/or miR-200b. A difference in biosignatures betweenresponders and non-responders can be used for theranosis. In anotherembodiment, vesicles are obtained from subjects having a disease orcondition. Vesicles are also obtained from subjects free of such diseaseor condition. The vesicles from both groups of subjects are assayed forunique biosignatures that are associated with all subjects in that groupbut not in subjects from the other group. Such biosignatures orbiomarkers can then used as a diagnostic for the presence or absence ofthe condition or disease, or to classify the subject as belonging on oneof the groups (those with/without disease, aggressive/non-aggressivedisease, responder/non-responder, etc).

In a further example, vesicles are assayed from patients having a stageI cancer and patients having stage II or III of the same cancer. Adifference in biosignatures or biomarkers between vesicles from eachgroup of patient is identified (e.g., vesicles from stage III cancer mayhave an increased expression of one or more genes or miR's), therebyidentifying a biosignature or biomarker that distinguishes differentstages of a disease. Such biosignature can then be used to prognosepatients having the disease.

In some instances, a biosignature is determined by assaying vesiclesfrom a subject over a period of time (e.g., every day, week, month, oryear). Thus, responders and non-responders or patients in phase I andphase II/III can have their vesicles interrogated over time (e.g., everymonth). The payload or physical attributes of the vesicles in each pointin time can be compared. A temporal pattern can thus form a biosignaturethat can then be used for theranosis, diagnosis, prognosis, diseasestratification, treatment monitoring, disease monitoring or making aprediction of responder/non-responder status. As a non-limiting example,an increasing amount of a biomarker (e.g., miR 122) in vesiscles over atime course can be associated with metastatic cancer, as opposed to astagnant amounts of the biomarker in vesiscles over the time course canbe associated with non-metastatic cancer. A time course may last over atleast 1 week, 2 weeks, 3 weeks, 4 weeks, 1 month, 6 weeks, 8 weeks, 2months, 10 weeks, 12 weeks, 3 months, 4 months, 5 months, 6 months, 7months, 8 months, 9 months, 10 months, 11 months, or at least 12 months.

The level of vesicles, level of vesicles with a specific biosignature,or a biosignature of a vesicle can also be used to assess the efficacyof a therapy for a condition. For example, the level of vesicles, levelof vesicles with a specific biosignature, or a biosignature of a vesiclecan be used to assess the efficacy of a cancer treatment, e.g.,chemotherapy, radiation therapy, surgery, or any other therapeuticapproach useful for inhibiting cancer in a subject. In addition, abiosignature can be used in a screening assay to identify candidate ortest compounds or agents (e.g., proteins, peptides, peptidomimetics,peptoids, small molecules or other drugs) that have a modulatory effecton the biosignature of a vesicle. Compounds identified via suchscreening assays may be useful, for example, for modulating, e.g.,inhibiting, ameliorating, treating, or preventing conditions ordiseases.

For example, a biosignature for a vesicle can be obtained from a patientwho is undergoing successful treatment for a particular cancer. Cellsfrom a cancer patient not being treated with the same drug can becultured and vesicles from the cultures obtained for determiningbiosignatures. The cells can be treated with test compounds and thebiosignature of the vesicles from the cultures can be compared to thebiosignature of the vesicles obtained from the patient undergoingsuccessful treatment. The test compounds that results in biosignaturesthat are similar to those of the patient undergoing successful treatmentcan be selected for further studies.

The biosignature of a vesicle can also be used to monitor the influenceof an agent (e.g., drug compounds) on the biosignature in clinicaltrials. Monitoring the level of vesicles, changes in the biosignature ofa vesicle, or both, can also be used in a method of assessing theefficacy of a test compound, such as a test compound for inhibitingcancer cells.

The level of vesicles, the biosignature of a vesicle, or both, can alsobe used to determine the effectiveness of a particular therapeuticintervention (pharmaceutical or non-pharmaceutical) and to alter theintervention to 1) reduce the risk of developing adverse outcomes, 2)enhance the effectiveness of the intervention or 3) identify resistantstates. Thus, in addition to diagnosing or confirming the presence of orrisk for developing a disease, condition or a syndrome, the methods andcompositions disclosed herein also provide a system for optimizing thetreatment of a subject having such a disease, condition or syndrome. Forexample, a therapy-related approach to treating a disease, condition orsyndrome by integrating diagnostics and therapeutics to improve thereal-time treatment of a subject can be determined by identifying thebiosignature of a vesicle.

Tests that identify the level of vesicles, the biosignature of avesicle, or both, can be used to identify which patients are most suitedto a particular therapy, and provide feedback on how well a drug isworking, so as to optimize treatment regimens. For example, inpregnancy-induced hypertension and associated conditions,therapy-related diagnostics can flexibly monitor changes in importantparameters (e.g., cytokine and/or growth factor levels) over time, tooptimize treatment.

Within the clinical trial setting of investigational agents as definedby the FDA, MDA, EMA, USDA, and EMEA, therapy-related diagnostics asdetermined by a biosignature disclosed herein, can provide keyinformation to optimize trial design, monitor efficacy, and enhance drugsafety. For instance, for trial design, therapy-related diagnostics canbe used for patient stratification, determination of patient eligibility(inclusion/exclusion), creation of homogeneous treatment groups, andselection of patient samples that are optimized to a matched casecontrol cohort. Such therapy-related diagnostic can therefore providethe means for patient efficacy enrichment, thereby minimizing the numberof individuals needed for trial recruitment. For example, for efficacy,therapy-related diagnostics are useful for monitoring therapy andassessing efficacy criteria. Alternatively, for safety, therapy-relateddiagnostics can be used to prevent adverse drug reactions or avoidmedication error and monitor compliance with the therapeutic regimen.

In some embodiments, the invention provides a method of identifyingresponder and non-responders to a treatment undergoing clinical trials,comprising detecting biosignatures comprising microRNA in subjectsenrolled in the clinical trial, and identifying biosignatures thatdistinguish between responders and non-responders. In a furtherembodiment, the biosignatures are measured in a drug naive subject andused to predict whether the subject will be a responder ornon-responder. The prediction can be based upon whether thebiosignatures of the drug naive subject correlate more closely with theclinical trial subjects identified as responders, thereby predictingthat the drug naive subject will be a responder. Conversely, if thebiosignatures of the drug naive subject correlate more closely with theclinical trial subjects identified as non-responders, the methods of theinvention can predict that the drug naive subject will be anon-responder. The prediction can therefore be used to stratifypotential responders and non-responders to the treatment. In someembodiments, the prediction is used to guide a course of treatment,e.g., by helping treating physicians decide whether to administer thedrug. In some embodiments, the prediction is used to guide selection ofpatients for enrollment in further clinical trials. In a non-limitingexample, biosignatures that predict responder/non-responder status inPhase II trials can be used to select patients for a Phase III trial,thereby increasing the likelihood of response in the Phase III patientpopulation. One of skill will appreciate that the method can be adaptedto identify biosignatures to stratify subjects on criteria other thanresponder/non-responder status. In one embodiment, the criterion istreatment safety. Therefore the method is followed as above to identifysubjects who are likely or not to have adverse events to the treatment.In a non-limiting example, biosignatures that predict safety profile inPhase II trials can be used to select patients for a Phase III trial,thereby increasing the treatment safety profile in the Phase III patientpopulation.

Therefore, the level of vesicles, the biosignature of a vesicle, orboth, can be used to monitor drug efficacy, determine response orresistance to a given drug, or both, thereby enhancing drug safety. Forexample, in colon cancer, vesicles are typically shed from colon cancercells and can be isolated from the peripheral blood and used to isolateone or more biomarkers e.g., KRAS mRNA which can then be sequenced todetect KRAS mutations. In the case of mRNA biomarkers, the mRNA can bereverse transcribed into cDNA and sequenced (e.g., by Sanger sequencing,pyrosequencing, NextGen sequencing, RT-PCR assays) to determine if thereare mutations present that confer resistance to a drug (e.g., cetuximabor panitumimab). In another example, vesicles that are specifically shedfrom lung cancer cells are isolated from a biological sample and used toisolate a lung cancer biomarker, e.g., EGFR mRNA. The EGFR mRNA isprocessed to cDNA and sequenced to determine if there are EGFR mutationspresent that show resistance or response to specific drugs or treatmentsfor lung cancer.

One or more biosignatures can be grouped so that information obtainedabout the set of biosignatures in a particular group provides areasonable basis for making a clinically relevant decision, such as butnot limited to a diagnosis, prognosis, or management of treatment, suchas treatment selection.

As with most diagnostic markers, it is often desirable to use the fewestnumber of markers sufficient to make a correct medical judgment. Thisprevents a delay in treatment pending further analysis as wellinappropriate use of time and resources.

Also disclosed herein are methods of conducting retrospective analysison samples (e.g., serum and tissue biobanks) for the purpose ofcorrelating qualitative and quantitative properties, such asbiosignatures of vesicles, with clinical outcomes in terms of diseasestate, disease stage, progression, prognosis; therapeutic efficacy orselection; or physiological conditions. Furthermore, methods andcompositions disclosed herein are utilized for conducting prospectiveanalysis on a sample (e.g., serum and/or tissue collected fromindividuals in a clinical trial) for the purpose of correlatingqualitative and quantitative biosignatures of vesicles with clinicaloutcomes in terms of disease state, disease stage, progression,prognosis; therapeutic efficacy or selection; or physiologicalconditions can also be performed. As used herein, a biosignature for avesicle can be used to identify a cell-of-origin specific vesicle.Furthermore, a biosignature can be determined based on a surface markerprofile of a vesicle or contents of a vesicle.

The biosignatures used to characterize a phenotype according to theinvention can comprise multiple components (e.g., microRNA, vesicles orother biomarkers) or characteristics (e.g., vesicle size or morphology).The biosignatures can comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 40, 50, 75, or 100components or characteristics. A biosignature with more than onecomponent or characteristic, such as at least 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 40, 50, 75, or 100components, may provide higher sensitivity and/or specificity incharacterizing a phenotype. In some embodiments, assessing a pluralityof components or characteristics provides increased sensitivity and/orspecificity as compared to assessing fewer components orcharacteristics. On the other hand, it is often desirable to use thefewest number of components or characteristics sufficient to make acorrect medical judgment. Fewer markers can avoid statisticaloverfitting of a classifier and can prevent a delay in treatment pendingfurther analysis as well inappropriate use of time and resources. Thus,the methods of the invention comprise determining an optimal number ofcomponents or characteristics.

A biosignature according to the invention can be used to characterize aphenotype with a sensitivity, specificity, accuracy, or similarperformance metric as described above. The biosignatures can also beused to build a classifier to classify a sample as belonging to a group,such as belonging to a group having a disease or not, a group having anaggressive disease or not, or a group of responders or non-responders.In one embodiment, a classifier is used to determine whether a subjecthas an aggressive or non-aggressive cancer. In the illustrative case ofprostate cancer, this can help a physician to determine whether to watchthe cancer, i.e., prescribe “watchful waiting,” or perform aprostatectomy. In another embodiment, a classifier is used to determinewhether a breast cancer patient is likely to respond or not totamoxifen, thereby helping the physician to determine whether or not totreat the patient with tamoxifen or another drug.

Biomarkers

A biosignature used to characterize a phenotype can comprise one or morebiomarkers. The biomarker can be a circulating marker, a membraneassociated marker, or a component present within a vesicle or on avesicle's surface. These biomarkers include without limitation a nucleicacid (e.g. RNA (mRNA, miRNA, etc.) or DNA), protein, peptide,polypeptide, antigen, lipid, carbohydrate, or proteoglycan.

The biosignature can include the presence or absence, expression level,mutational state, genetic variant state, or any modification (such asepigentic modification, post-translation modification) of a biomarker(e.g. any one or more biomarker listed in FIGS. 1, 3-60). The expressionlevel of a biomarker can be compared to a control or reference, todetermine the overexpression or underexpression (or upregulation ordownregulation) of a biomarker in a sample. In some embodiments, thecontrol or reference level comprises the amount of a same biomarker,such as a miRNA, in a control sample from a subject that does not haveor exhibit the condition or disease. In another embodiment, the controlof reference levels comprises that of a housekeeping marker whose levelis minimally affected, if at all, in different biological settings suchas diseased versus non-diseased states. In yet another embodiment, thecontrol or reference level comprises that of the level of the samemarker in the same subject but in a sample taken at a different timepoint. Other types of controls are described herein.

Nucleic acid biomarkers include various RNA or DNA species. For example,the biomarker can be mRNA, microRNA (miRNA), small nucleolar RNAs(snoRNA), small nuclear RNAs (snRNA), ribosomal RNAs (rRNA),heterogeneous nuclear RNA (hnRNA), ribosomal RNAS (rRNA), siRNA,transfer RNAs (tRNA), or shRNA. The DNA can be double-stranded DNA,single stranded DNA, complementary DNA, or noncoding DNA. miRNAs areshort ribonucleic acid (RNA) molecules which average about 22nucleotides long. miRNAs act as post-transcriptional regulators thatbind to complementary sequences in the three prime untranslated regions(3′ UTRs) of target messenger RNA transcripts (mRNAs), which can resultin gene silencing. One miRNA may act upon 1000s of mRNAs. miRNAs playmultiple roles in negative regulation, e.g., transcript degradation andsequestering, translational suppression, and may also have a role inpositive regulation, e.g., transcriptional and translational activation.By affecting gene regulation, miRNAs can influence many biologicprocesses. Different sets of expressed miRNAs are found in differentcell types and tissues.

Biomarkers for use with the invention further include peptides,polypeptides, or proteins, which terms are used interchangeablythroughout unless otherwise noted. In some embodiments, the proteinbiomarker comprises its modification state, truncations, mutations,expression level (such as overexpression or underexpression as comparedto a reference level), and/or post-translational modifications, such asdescribed above. In a non-limiting example, a biosignature for a diseasecan include a protein having a certain post-translational modificationthat is more prevalent in a sample associated with the disease thanwithout.

A biosignature may include a number of the same type of biomarkers(e.g., two different microRNA species) or one or more of different typesof biomarkers (e.g. mRNAs, miRNAs, proteins, peptides, ligands, andantigens).

One or more biosignatures can comprise at least one biomarker selectedfrom those listed in FIGS. 1, 3-60. A specific cell-of-originbiosignature may include one or more biomarkers. FIGS. 3-58 depicttables which lists a number of disease or condition specific biomarkersthat can be derived and analyzed from a vesicle. The biomarker can alsobe CD24, midkine, hepcidin, TMPRSS2-ERG, PCA-3, PSA, EGFR, EGFRvIII,BRAF variant, MET, cKit, PDGFR, Wnt, beta-catenin, K-ras, H-ras, N-ras,Raf, N-myc, c-myc, IGFR, PI3K, Akt, BRCA1, BRCA2, PTEN, VEGFR-2,VEGFR-1, Tie-2, TEM-1, CD276, HER-2, HER-3, or HER-4. The biomarker canalso be annexin V, CD63, Rab-5b, or caveolin, or a miRNA, such aslet-7a; miR-15b; miR-16; miR-19b; miR-21; miR-26a; miR-27a; miR-92;miR-93; miR-320 or miR-20. The biomarker can also be of any gene orfragment thereof as disclosed in PCT Publication No. WO2009/100029, suchas those listed in Tables 3-15 therein.

Other biomarkers useful for assessment in methods and compositionsdisclosed herein include those associated with conditions orphysiological states as disclosed in U.S. Pat. Nos. 6,329,179 and7,625,573; U.S. Patent Publication Nos. 2002/106684, 2004/005596,2005/0159378, 2005/0064470, 2006/116321, 2007/0161004, 2007/0077553,2007/104738, 2007/0298118, 2007/0172900, 2008/0268429, 2010/0062450,2007/0298118, 2009/0220944 and 2010/0196426; U.S. patent applicationSer. Nos. 12/524,432, 12/524,398, 12/524,462; Canadian Patent CA2453198; and International PCT Patent Publication Nos. WO1994022018,WO2001036601, WO2003063690, WO2003044166, WO2003076603, WO2005121369,WO2005118806, WO/2005/078124, WO2007126386, WO2007088537, WO2007103572,WO2009019215, WO2009021322, WO2009036236, WO2009100029, WO2009015357,WO2009155505, WO 2010/065968 and WO 2010/070276; each of which patent orapplication is incorporated herein by reference in their entirety. Thebiomarkers disclosed in these patents and applications, includingvesicle biomarkers and microRNAs, can be assessed as part of a signaturefor characterizing a phenotype, such as providing a diagnosis, prognosisor theranosis of a cancer or other disease. Furthermore, the methods andtechniques disclosed therein can be used to assess biomarkers, includingvesicle biomarkers and microRNAs.

Another group of useful biomarkers for assessment in methods andcompositions disclosed herein include those associated with cancerdiagnostics, prognostics and theranostics as disclosed in U.S. Pat. Nos.6,692,916, 6,960,439, 6,964,850, 7,074,586; U.S. patent application Ser.Nos. 11/159,376, 11/804,175, 12/594,128, 12/514,686, 12/514,775,12/594,675, 12/594,911, 12/594,679, 12/741,787, 12/312,390; andInternational PCT Patent Application Nos. PCT/US2009/049935,PCT/US2009/063138, PCT/US2010/000037; each of which patent orapplication is incorporated herein by reference in their entirety.Usefule biomarkers further include those described in U.S. patentapplication Ser. No. 10/703,143 and U.S. Ser. No. 10/701,391 forinflammatory disease; Ser. No. 11/529,010 for rheumatoid arthritis; Ser.No. 11/454,553 and Ser. No. 11/827,892 for multiple sclerosis; Ser. No.11/897,160 for transplant rejection; Ser. No. 12/524,677 for lupus;PCT/US2009/048684 for osteoarthritis; Ser. No. 10/742,458 for infectiousdisease and sepsis; Ser. No. 12/520,675 for sepsis; each of which patentor application is incorporated herein by reference in their entirety.The biomarkers disclosed in these patents and applications, includingmRNAs, can be assessed as part of a signature for characterizing aphenotype, such as providing a diagnosis, prognosis or theranosis of acancer or other disease. Furthermore, the methods and techniquesdisclosed therein can be used to assess biomarkers, including vesiclebiomarkers and microRNAs.

Still other biomarkers useful for assessment in methods and compositionsdisclosed herein include those associated with conditions orphysiological states as disclosed in Wieczorek et al., Isolation andcharacterization of an RNA-proteolipid complex associated with themalignant state in humans, Proc Natl Acad Sci USA. 1985 May;82(10):3455-9; Wieczorek et al., Diagnostic and prognostic value ofRNA-proteolipid in sera of patients with malignant disorders followingtherapy: first clinical evaluation of a novel tumor marker, Cancer Res.1987 Dec. 1; 47(23):6407-12; Escola et al. Selective enrichment oftetraspan proteins on the internal vesicles of multivesicular endosomesand on exosomes secreted by human B-lymphocytes. J. Biol. Chem. (1998)273:20121-27; Pileri et al. Binding of hepatitis C virus to CD81Science, (1998) 282:938-41); Kopreski et al. Detection of TumorMessenger RNA in the Serum of Patients with Malignant Melanoma, Clin.Cancer Res. (1999) 5:1961-1965; Carr et al. Circulating MembraneVesicles in Leukemic Blood, Cancer Research, (1985) 45:5944-51; Weichertet al. Cytoplasmic CD24 expression in colorectal cancer independentlycorrelates with shortened patient survival. Clinical Cancer Research,2005, 11:6574-81); Iorio et al. MicroRNA gene expression deregulation inhuman breast cancer. Cancer Res (2005) 65:7065-70; Taylor et al.Tumour-derived exosomes and their role in cancer-associated T-cellsignaling defects British J Cancer (2005) 92:305-11; Valadi et al.Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism ofgenetic exchange between cells Nature Cell Biol (2007) 9:654-59; Tayloret al. Pregnancy-associated exosomes and their modulation of T cellsignaling J Immunol (2006) 176:1534-42; Koga et al. Purification,characterization and biological significance of tumor-derived exosomesAnticancer Res (2005) 25:3703-08; Seligson et al. Epithelial celladhesion molecule (ESA) expression: pathobiology and its role as anindependent predictor of survival in renal cell carcinoma Clin CancerRes (2004) 10:2659-69; Clayton et al. (Antigen-presenting cell exosomesare protected from complement-mediated lysis by expression of CD55 andCD59. Eur J Immunol (2003) 33:522-31); Simak et al. Cell MembraneMicroparticles in Blood and Blood Products: Potentially PathogenicAgents and Diagnostic Markers Trans Med Reviews (2006) 20:1-26; Choi etal. Proteomic analysis of microvesicles derived from human colorectalcancer cells J Proteome Res (2007) 6:4646-4655; Iero et al.Tumour-released exosomes and their implications in cancer immunity CellDeath Diff (2008) 15:80-88; Baj-Krzyworzeka et al. Tumour-derivedmicrovesicles carry several surface determinants and mRNA of tumourcells and transfer some of these determinants to monocytes CencerImmunol Immunother (2006) 55:808-18; Admyre et al. B cell-derivedexosomes can present allergen peptides and activate allergen-specific Tcells to proliferate and produce TH2-like cytokines J Allergy ClinImmunol (2007) 120:1418-1424; Aoki et al. Identification andcharacterization of microvesicles secreted by 3T3-L1 adipocytes:redox-and hormone dependent induction of milk fat globule-epidermalgrowth factor 8-associated microvesicles Endocrinol (2007)148:3850-3862; Baj-Krzyworzeka et al. Tumour-derived microvesicles carryseveral surface determinants and mRNA of tumour cells and transfer someof these determinants to monocytes Cencer Immunol Immunother (2006)55:808-18; Skog et al. Glioblastoma microvesicles transport RNA andproteins that promote tumour growth and provide diagnostic biomarkersNature Cell Biol (2008) 10:1470-76; El-Hefnawy et al. Characterizationof amplifiable, circulating RNA in plasma and its potential as a toolfor cancer diagnostics Clin Chem (2004) 50:564-573; Pisitkun et al.,Proc Natl Acad Sci USA, 2004; 101:13368-13373; Mitchell et al., Canurinary exosomes act as treatment response markers in Prostate Cancer?,Journal of Translational Medicine 2009, 7:4; Clayton et al., HumanTumor-Derived Exosomes Selectively Impair Lymphocyte Responses toInterleukin-2, Cancer Res 2007; 67: (15). Aug. 1, 2007; Rabesandratanaet al. Decay-accelerating factor (CD55) and membrane inhibitor ofreactive lysis (CD59) are released within exosomes during In vitromaturation of reticulocytes. Blood 91:2573-2580 (1998); Lamparski et al.Production and characterization of clinical grade exosomes derived fromdendritic cells. J Immunol Methods 270:211-226 (2002); Keller et al.CD24 is a marker of exosomes secreted into urine and amniotic fluid.Kidney Int'l 72:1095-1102 (2007); Runz et al. Malignant ascites-derivedexosomes of ovarian carcinoma patients contain CD24 and EpCAM. Gyn Oncol107:563-571 (2007); Redman et al. Circulating microparticles in normalpregnancy and preeclampsia placenta. 29:73-77 (2008); Gutwein et al.Cleavage of L 1 in exosomes and apoptotic membrane vesicles releasedfrom ovarian carcinoma cells. Clin Cancer Res 11:2492-2501 (2005);Kristiansen et al., CD24 is an independent prognostic marker of survivalin nonsmall cell lung cancer patients, Brit J Cancer 88:231-236 (2003);Lim and Oh, The Role of CD24 in Various Human Epithelial Neoplasias,Pathol Res Pract 201:479-86 (2005); Matutes et al., The Immunophenotypeof Splenic Lymphoma with Villous Lymphocytes and its Relevance to theDifferential Diagnosis With Other B-Cell Disorders, Blood 83:1558-1562(1994); Pirruccello and Lang, Differential Expression of CD24-RelatedEpitopes in Mycosis Fungoides/Sezary Syndrome: A Potential Marker forCirculating Sezary Cells, Blood 76:2343-2347 (1990). The biomarkersdisclosed in these publications, including vesicle biomarkers andmicroRNAs, can be assessed as part of a signature for characterizing aphenotype, such as providing a diagnosis, prognosis or theranosis of acancer or other disease. Furthermore, the methods and techniquesdisclosed therein can be used to assess biomarkers, including vesiclebiomarkers and microRNAs.

Still other biomarkers useful for assessment in methods and compositionsdisclosed herein include those associated with conditions orphysiological states as disclosed in Rajendran et al., Proc Natl AcadSci USA 2006; 103:11172-11177, Taylor et al., Gynecol Oncol 2008;110:13-21, Zhou et al., Kidney Int 2008; 74:613-621, Buning et al.,Immunology 2008, Prado et al. J Immunol 2008; 181:1519-1525, Vella etal. (2008) Vet Immunol Immunopathol 124(3-4): 385-93, Gould et al.(2003). Proc Natl Acad Sci USA 100(19): 10592-7, Fang et al. (2007).PLoS Biol 5(6): e158, Chen, B. J and R. A. Lamb (2008). Virology 372(2):221-32, Bhatnagar, S, and J. S. Schorey (2007). J Biol Chem 282(35):25779-89, Bhatnagar et al. (2007) Blood 110(9): 3234-44, Yuyama, et al.(2008). J Neurochem 105(1): 217-24, Gomes et al. (2007). Neurosci Lett428(1): 43-6, Nagahama et al. (2003). Autoimmunity 36(3): 125-31,Taylor, D. D., S. Akyol, et al. (2006). J Immunol 176(3): 1534-42,Peche, et al. (2006). Am J Transplant 6(7): 1541-50, Zero, M., M.Valenti, et al. (2008). Cell Death and Differentiation 15: 80-88,Gesierich, S., I. Berezoversuskiy, et al. (2006), Cancer Res 66(14):7083-94, Clayton, A., A. Turkes, et al. (2004). Faseb J 18(9): 977-9,Skriner., K. Adolph, et al. (2006). Arthritis Rheum 54(12): 3809-14,Brouwer, R., G. J Pruijn, et al. (2001). Arthritis Res 3(2): 102-6, Kim,S. H, N Bianco, et al. (2006). Mol Ther 13(2): 289-300, Evans, C. H, S.C. Ghivizzani, et al. (2000). Clin Orthop Relat Res (379 Suppl): S300-7,Zhang, H G., C. Liu, et al. (2006). J Immunol 176(12): 7385-93, VanNiel, G., J. Mallegol, et al. (2004). Gut 52: 1690-1697, Fiasse, R. andO. Dewit (2007). Expert Opinion on Therapeutic Patents 17(12):1423-1441(19). The biomarkers disclosed in these publications, includingvesicle biomarkers and microRNAs, can be assessed as part of a signaturefor characterizing a phenotype, such as providing a diagnosis, prognosisor theranosis of a cancer or other disease. Furthermore, the methods andtechniques disclosed therein can be used to assess biomarkers, includingvesicle biomarkers and microRNAs.

A biomarker that can be derived and analyzed from a vesicle is miRNA(miR), miRNA*nonsense (miR*), and other RNAs (including, but not limitedto, mRNA, preRNA, priRNA, hnRNA, snRNA, siRNA, shRNA). A miRNA biomarkerincludes not only its miRNA and microRNA* nonsense, but its precursormolecules: pri-microRNAs (pri-miRs) and pre-microRNAs (pre-miRs). Thesequence of a miRNA can be obtained from publicly available databasessuch as http://www.mirbase.org/, http://www.microrna.org/, or any othersavailable. The biomarker can also be a nucleic acid molecule (e.g. DNA),protein, or peptide. The presence or absence, expression level,mutations (for example genetic mutations, such as deletions,translocations, duplications, nucleotide or amino acid substitutions,and the like) can be determined for the biomarker. Any epigeneticmodulation or copy number variation of a biomarker can also be analyzed.

The one or more biomarkers analyzed can be indicative of a particulartissue or cell of origin, disease, or physiological state. Furthermore,the presence, absence or expression level of one or more of thebiomarkers described herein can be correlated to a phenotype of asubject, including a disease, condition, prognosis or drug efficacy. Thespecific biomarker and biosignature set forth below constitutenon-inclusive examples for each of the diseases, condition comparisons,conditions, and/or physiological states. Furthermore, the one or morebiomarker assessed for a phenotype can be a cell-of-origin specificvesicle.

The one or more miRNAs used to characterize a phenotype may be selectedfrom those disclosed in PCT Publication No. WO2009/036236. For example,one or more miRNAs listed in Tables I-VI (FIGS. 6-11) therein can beused to characterize colon adenocarcinoma, colorectal cancer, prostatecancer, lung cancer, breast cancer, b-cell lymphoma, pancreatic cancer,diffuse large BCL cancer, CLL, bladder cancer, renal cancer,hypoxia-tumor, uterine leiomyomas, ovarian cancer, hepatitis Cvirus-associated hepatocellular carcinoma, ALL, Alzheimer's disease,myelofibrosis, myelofibrosis, polycythemia vera, thrombocythemia, HIV,or HIV-I latency, as further described herein.

The one or more miRNAs can be detected in a vesicle. The one or moremiRNAs can be miR-223, miR-484, miR-191, miR-146a, miR-016, miR-026a,miR-222, miR-024, miR-126, and miR-32. One or more miRNAs can also bedetected in PBMC. The one or more miRNAs can be miR-223, miR-150,miR-146b, miR-016, miR-484, miR-146a, miR-191, miR-026a, miR-019b, ormiR-020a. The one or more miRNAs can be used to characterize aparticular disease or condition. For example, for the disease bladdercancer, one or more miRNAs can be detected, such as miR-223, miR-26b,miR-221, miR-103-1, miR-185, miR-23b, miR-203, miR-17-5p, miR-23a,miR-205 or any combination thereof. The one or more miRNAs may beupregulated or overexpressed.

In some embodiments, the one or more miRNAs is used to characterizehypoxia-tumor. The one or more miRNA may be miR-23, miR-24, miR-26,miR-27, miR-103, miR-107, miR-181, miR-210, or miR-213, and may beupregulated. One or more miRNAs can also be used to characterize uterineleiomyomas. For example, the one or more miRNAs used to characterize auterine leiomyoma may be a let-7 family member, miR-21, miR-23b,miR-29b, or miR-197. The miRNA can be upregulated.

Myelofibrosis can also be characterized by one or more miRNAs, such asmiR-190, which can be upregulated; miR-31, miR-150 and miR-95, which canbe downregulated, or any combination thereof. Furthermore,myelofibrosis, polycythemia vera or thrombocythemia can also becharacterized by detecting one or more miRNAs, such as, but not limitedto, miR-34a, miR-342, miR-326, miR-105, miR-149, miR-147, or anycombination thereof. The one or more miRNAs may be down-regulated.

Other examples of phenotypes that can be characterized by assessing avesicle for one or more biomarkers are further described herein.

The one or more biomarkers can be detected using a probe. A probe cancomprise an oligonucleotide, such as DNA or RNA, an aptamer, monoclonalantibody, polyclonal antibody, Fabs, Fab′, single chain antibody,synthetic antibody, peptoid, zDNA, peptide nucleic acid (PNA), lockednucleic acid (LNA), lectin, synthetic or naturally occurring chemicalcompound (including but not limited to a drug or labeling reagent),dendrimer, or a combination thereof. The probe can be directly detected,for example by being directly labeled, or be indirectly detected, suchas through a labeling reagent. The probe can selectively recognize abiomarker. For example, a probe that is an oligonucleotide canselectively hybridize to a miRNA biomarker.

In aspects, the invention provides for the diagnosis, theranosis,prognosis, disease stratification, disease staging, treatment monitoringor predicting responder/non-responder status of a disease or disorder ina subject. The invention comprises assessing vesicles from a subject,including assessing biomarkers present on the vesicles and/or assessingpayload within the vesicles, such as protein, nucleic acid or otherbiological molecules. Any appropriate biomarker that can be assessedusing a vesicle and that relates to a disease or disorder can be usedthe carry out the methods of the invention. Furthermore, any appropriatetechnique to assess a vesicle as described herein can be used. Exemplarybiomarkers for specific diseases that can be assessed according to themethods of the invention include the following:

Breast Cancer

Breast cancer specific biomarkers can include one or more (for example,2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs,mRNA, genetic mutations, proteins, ligands, peptides, snoRNA, or anycombination thereof, such as listed in FIG. 3.

One or more breast cancer specific biomarker can be assessed to providea breast cancer specific biosignature. For example, the biosignature cancomprise one or more overexpressed miRs, including but not limited to,miR-21, miR-155, miR-206, miR-122a, miR-210, miR-21, miR-21, miR-155,miR-206, miR-122a, miR-210, or miR-21, or any combination thereof.

The biosignature can also comprise one or more underexpressed miRs suchas, but not limited to, let-7, miR-10b, miR-125a, miR-125b, miR-145,miR-143, miR-145, miR-16, let-7, let-7, let-7, miR-10b, miR-125a,miR-125b, or miR-145, or any combination thereof.

The mRNAs that may be analyzed can include, but are not limited to, ER,PR, HER2, MUC1, or EGFR, or any combination thereof. Mutationsincluding, but not limited to, those related to KRAS, B-Raf, or CYP2D6,or any combination thereof can also be used as specific biomarkers froma vesicle for breast cancer. In addition, a protein, ligand, or peptidethat can be used as biomarkers from a vesicle that is specific to breastcancer includes, but are not limited to, hsp70, MART-1, TRP, HER2,hsp70, MART-1, TRP, HER2, ER, PR, Class III b-tubulin, or VEGFA, or anycombination thereof. Furthermore the snoRNA that can be used as anexosomal biomarker for breast cancer include, but are not limited to,GAS5. The gene fusion ETV6-NTRK3 can also be used a biomarker for breastcancer.

The invention also provides an isolated vesicle comprising one or morebreast cancer specific biomarkers, such as ETV6-NTRK3, or biomarkerslisted in FIG. 3 and in FIG. 1 for breast cancer. A compositioncomprising the isolated vesicle is also provided. Accordingly, in someembodiments, the composition comprises a population of vesiclescomprising one or more breast cancer specific biomarkers, such asETV6-NTRK3, or biomarkers listed in FIG. 3 and in FIG. 1 for breastcancer. The composition can comprise a substantially enriched populationof vesicles, wherein the population of vesicles is substantiallyhomogeneous for breast cancer specific vesicles or vesicles comprisingone or more breast cancer specific biomarkers, such as ETV6-NTRK3, orbiomarkers listed in FIG. 3 and in FIG. 1 for breast cancer.

One or more breast cancer specific biomarkers, such as ETV6-NTRK3, orbiomarkers listed in FIG. 3 and in FIG. 1 for breast cancer can also bedetected by one or more systems disclosed herein, for characterizing abreast cancer. For example, a detection system can comprise one or moreprobes to detect one or more breast cancer specific biomarkers, such asETV6-NTRK3, or biomarkers listed in FIG. 3 and in FIG. 1 for breastcancer, of one or more vesicles of a biological sample.

Ovarian Cancer

Ovarian cancer specific biomarkers from a vesicle can include one ormore (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 4,and can be used to create a ovarian cancer specific biosignature. Forexample, the biosignature can comprise one or more overexpressed miRs,such as, but not limited to, miR-200a, miR-141, miR-200c, miR-200b,miR-21, miR-141, miR-200a, miR-200b, miR-200c, miR-203, miR-205,miR-214, miR-199*, or miR-215, or any combination thereof. Thebiosignature can also comprise one or more underexpressed miRs such as,but not limited to, miR-199a, miR-140, miR-145, miR-100, miR-let-7cluster, or miR-125b-1, or any combination thereof. The one or moremRNAs that may be analyzed can include without limitation ERCC1, ER,TOPO1, TOP2A, AR, PTEN, HER2/neu, CD24 or EGFR, or any combinationthereof.

A biomarker mutation for ovarian cancer that can be assessed in avesicle includes, but is not limited to, a mutation of KRAS, mutation ofB-Raf, or any combination of mutations specific for ovarian cancer. Theprotein, ligand, or peptide that can be assessed in a vesicle caninclude, but is not limited to, VEGFA, VEGFR2, or HER2, or anycombination thereof. Furthermore, a vesicle isolated or assayed can beovarian cancer cell specific, or derived from ovarian cancer cells.

The invention also provides an isolated vesicle comprising one or moreovarian cancer specific biomarkers, such as CD24, those listed in FIG. 4and in FIG. 1 for ovarian cancer. A composition comprising the isolatedvesicle is also provided. Accordingly, in some embodiments, thecomposition comprises a population of vesicles comprising one or moreovarian cancer specific biomarkers, such as CD24, those listed in FIG. 4and in FIG. 1 for ovarian cancer. The composition can comprise asubstantially enriched population of vesicles, wherein the population ofvesicles is substantially homogeneous for ovarian cancer specificvesicles or vesicles comprising one or more ovarian cancer specificbiomarkers, such as CD24, those listed in FIG. 4 and in FIG. 1 forovarian cancer.

One or more ovarian cancer specific biomarkers, such as CD24, thoselisted in FIG. 4 and in FIG. 1 for ovarian cancer can also be detectedby one or more systems disclosed herein, for characterizing an ovariancancer. For example, a detection system can comprise one or more probesto detect one or more ovarian cancer specific biomarkers, such as CD24,those listed in FIG. 4 and in FIG. 1 for ovarian cancer, of one or morevesicles of a biological sample.

Lung Cancer

Lung cancer specific biomarkers from a vesicle can include one or more(for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 5,and can be used to create a lung cancer specific biosignature.

The biosignature can comprise one or more overexpressed miRs, such as,but not limited to, miR-21, miR-205, miR-221 (protective), let-7a(protective), miR-137 (risky), miR-372 (risky), or miR-122a (risky), orany combination thereof. The biosignature can comprise one or moreupregulated or overexpressed miRNAs, such as miR-17-92, miR-19a, miR-21,miR-92, miR-155, miR-191, miR-205 or miR-210; one or more downregulatedor underexpressed miRNAs, such as miR-let-7, or any combination thereof.The one or more biomarker may be miR-92a-2*, miR-147, miR-574-5p, suchas for small cell lung cancer.

The one or more mRNAs that may be analyzed can include, but are notlimited to, EGFR, PTEN, RRM1, RRM2, ABCB1, ABCG2, LRP, VEGFR2, VEGFR3,class III b-tubulin, or any combination thereof.

A biomarker mutation for lung cancer that can be assessed in a vesicleincludes, but is not limited to, a mutation of EGFR, KRAS, B-Raf,UGT1A1, or any combination of mutations specific for lung cancer. Theprotein, ligand, or peptide that can be assessed in a vesicle caninclude, but is not limited to, KRAS, hENT1, or any combination thereof.

The biomarker can also be midkine (MK or MDK). Furthermore, a vesicleisolated or assayed can be lung cancer cell specific, or derived fromlung cancer cells.

The invention also provides an isolated vesicle comprising one or morelung cancer specific biomarkers, such as RLF-MYCL1, TGF-ALK, orCD74-ROS1, or those listed in FIG. 5 and in FIG. 1 for lung cancer. Acomposition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more lung cancer specific biomarkers, suchas RLF-MYCL1, TGF-ALK, or CD74-ROS1, or those listed in FIG. 5 and inFIG. 1 for lung cancer. The composition can comprise a substantiallyenriched population of vesicles, wherein the population of vesicles issubstantially homogeneous for lung cancer specific vesicles or vesiclescomprising one or more lung cancer specific biomarkers, such asRLF-MYCL1, TGF-ALK, or CD74-ROS1, or those listed in FIG. 5 and in FIG.1 for lung cancer.

One or more lung cancer specific biomarkers, such as RLF-MYCL1, TGF-ALK,or CD74-ROS1, or those listed in FIG. 5 and in FIG. 1 for lung cancercan also be detected by one or more systems disclosed herein, forcharacterizing a lung cancer. For example, a detection system cancomprise one or more probes to detect one or more lung cancer specificbiomarkers, such as RLF-MYCL1, TGF-ALK, or CD74-ROS1, or those listed inFIG. 5 and in FIG. 1 for lung cancer, of one or more vesicles of abiological sample.

Colon Cancer

Colon cancer specific biomarkers from a vesicle can include one or more(for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 6,and can be used to create a colon cancer specific biosignature. Forexample, the biosignature can comprise one or more overexpressed miRs,such as, but not limited to, miR-24-1, miR-29b-2, miR-20a, miR-10a,miR-32, miR-203, miR-106a, miR-17-5p, miR-30c, miR-223, miR-126,miR-128b, miR-21, miR-24-2, miR-99b, miR-155, miR-213, miR-150, miR-107,miR-191, miR-221, miR-20a, miR-510, miR-92, miR-513, miR-19a, miR-21,miR-20, miR-183, miR-96, miR-135b, miR-31, miR-21, miR-92, miR-222,miR-181b, miR-210, miR-20a, miR-106a, miR-93, miR-335, miR-338,miR-133b, miR-346, miR-106b, miR-153a, miR-219, miR-34a, miR-99b,miR-185, miR-223, miR-211, miR-135a, miR-127, miR-203, miR-212, miR-95,or miR-17-5p, or any combination thereof. The biosignature can alsocomprise one or more underexpressed miRs such as miR-143, miR-145,miR-143, miR-126, miR-34b, miR-34c, let-7, miR-9-3, miR-34a, miR-145,miR-455, miR-484, miR-101, miR-145, miR-133b, miR-129, miR-124a,miR-30-3p, miR-328, miR-106a, miR-17-5p, miR-342, miR-192, miR-1,miR-34b, miR-215, miR-192, miR-301, miR-324-5p, miR-30a-3p, miR-34c,miR-331, miR-548c-5p, miR-362-3p, miR-422a, or miR-148b, or anycombination thereof.

The one or more biomarker can be an upregulated or overexpressed miRNA,such as miR-20a, miR-21, miR-106a, miR-181b or miR-203, forcharacterizing a colon adenocarcinoma. The one or more biomarker can beused to characterize a colorectal cancer, such as an upregulated oroverexpressed miRNA selected from the group consisting of: miR-19a,miR-21, miR-127, miR-31, miR-96, miR-135b and miR-183, a downregulatedor underexpressed miRNA, such as miR-30c, miR-133a, mir143, miR-133b ormiR-145, or any combination thereof. The one or more biomarker can beused to characterize a colorectal cancer, such as an upregulated oroverexpressed miRNA selected from the group consisting of: miR-548c-5p,miR-362-3p, miR-422a, miR-597, miR-429, miR-200a, and miR-200b, or anycombination thereof.

The one or more mRNAs that may be analyzed can include, but are notlimited to, EFNB1, ERCC1, HER2, VEGF, or EGFR, or any combinationthereof. A biomarker mutation for colon cancer that can be assessed in avesicle includes, but is not limited to, a mutation of EGFR, KRAS,VEGFA, B-Raf, APC, or p53, or any combination of mutations specific forcolon cancer. The protein, ligand, or peptide that can be assessed in avesicle can include, but is not limited to, AFRs, Rabs, ADAM10, CD44,NG2, ephrin-B1, MIF, b-catenin, Junction, plakoglobin, glalectin-4,RACK1, tetrspanin-8, FasL, TRAIL, A33, CEA, EGFR, dipeptidase 1, hsc-70,tetraspanins, ESCRT, TS, PTEN, or TOPO1, or any combination thereof.Furthermore, a vesicle isolated or assayed can be colon cancer cellspecific, or derived from colon cancer cells.

The invention also provides an isolated vesicle comprising one or morecolon cancer specific biomarkers, such as listed in FIG. 6 and in FIG. 1for colon cancer. A composition comprising the isolated vesicle is alsoprovided. Accordingly, in some embodiments, the composition comprises apopulation of vesicles comprising one or more colon cancer specificbiomarkers, such as listed in FIG. 6 and in FIG. 1 for colon cancer. Thecomposition can comprise a substantially enriched population ofvesicles, wherein the population of vesicles is substantiallyhomogeneous for colon cancer specific vesicles or vesicles comprisingone or more colon cancer specific biomarkers, such as listed in FIG. 6and in FIG. 1 for colon cancer.

One or more colon cancer specific biomarkers, such as listed in FIG. 6and in FIG. 1 for colon cancer can also be detected by one or moresystems disclosed herein, for characterizing a colon cancer. Forexample, a detection system can comprise one or more probes to detectone or more colon cancer specific biomarkers, such as listed in FIG. 6and in FIG. 1 for colon cancer, of one or more vesicles of a biologicalsample.

Adenoma Versus Hyperplastic Polyp

Adenoma versus hyperplastic polyp specific biomarkers from a vesicle caninclude one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more)overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations,proteins, ligands, peptides, or any combination thereof, such as listedin FIG. 7, and can be used to create an adenoma versus hyperplasticpolyp specific biosignature. For example, the one or more mRNAs that maybe analyzed can include, but are not limited to, ABCA8, KIAA1199, GCG,MAMDC2, C2orf32, 229670_at, IGF1, PCDH7, PRDX6, PCNA, COX2, or MUC6, orany combination thereof.

A biomarker mutation to distinguish for adenoma versus hyperplasticpolyp that can be assessed in a vesicle includes, but is not limited to,a mutation of KRAS, mutation of B-Raf, or any combination of mutationsspecific for distinguishing between adenoma versus hyperplastic polyp.The protein, ligand, or peptide that can be assessed in a vesicle caninclude, but is not limited to, hTERT.

The invention also provides an isolated vesicle comprising one or morespecific biomarkers for distinguishing between an adenoma and ahyperplastic polyp, such as listed in FIG. 7. A composition comprisingthe isolated vesicle is also provided. Accordingly, in some embodiments,the composition comprises a population of vesicles comprising one ormore specific biomarkers for distinguishing between an adenoma and ahyperplastic polyp, such as listed in FIG. 7. The composition cancomprise a substantially enriched population of vesicles, wherein thepopulation of vesicles is substantially homogeneous for having one ormore specific biomarkers for distinguishing between an adenoma and ahyperplastic polyp, such as listed in FIG. 7.

One or more specific biomarkers for distinguishing between an adenomaand a hyperplastic polyp, such as listed in FIG. 7 can also be detectedby one or more systems disclosed herein, for distinguishing between anadenoma and a hyperplastic polyp. For example, a detection system cancomprise one or more probes to detect one or more specific biomarkersfor distinguishing between an adenoma and a hyperplastic polyp, such aslisted in FIG. 7, of one or more vesicles of a biological sample.

Bladder Cancer

Biomarkers for bladder cancer can be used to assess a bladder canceraccording to the methods of the invention. The biomarkers can includeone or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressedmiRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof. Biomarkers for bladdercancer include without limitation one or more of miR-223, miR-26b,miR-221, miR-103-1, miR-185, miR-23b, miR-203, miR-17-5p, miR-23a,miR-205 or any combination thereof. Further biomarkers for bladdercancer include FGFR3, EGFR, pRB (retinoblastoma protein), 5T4, p53,Ki-67, VEGF, CK20, COX2, p21, Cyclin D1, p14, p15, p16, Her-2, MAPK(mitogen-activated protein kinase), Bax/Bcl-2, PI3K(phosphoinositide-3-kinase), CDKs (cyclin-dependent kinases), CD40,TSP-1, HA-ase, telomerase, survivin, NMP22, TNF, Cyclin E1, p2′7,caspase, survivin, NMP22 (Nuclear matrix protein 22), BCLA-4,Cytokeratins (8, 18, 19 and 20), CYFRA 21-1, IL-2, and complement factorH-related protein. In an embodiment, non-receptor tyrosine kinaseETK/BMX and/or Carbonic Anhydrase IX is used as a marker of bladdercancer for diagnostic, prognostic and therapeutic purposes. See Guo etal., Tyrosine Kinase ETK/BMX Is Up-Regulated in Bladder Cancer andPredicts Poor Prognosis in Patients with Cystectomy. PLoS One. 2011 Mar.7; 6(3):e17778.; Klatte et al., Carbonic anhydrase IX in bladder cancer:a diagnostic, prognostic, and therapeutic molecular marker. Cancer. 2009Apr. 1; 115(7):1448-58. The biomarker can be one or more vesiclebiomarker associated with bladder cancer as described in Pisitkun etal., Discovery of urinary biomarkers. Mol Cell Proteomics. 2006 October;5(10):1760-71; Welton et al, Proteomics analysis of bladder cancerexosomes. Mol Cell Proteomics. 2010 June; 9(6):1324-38. These biomarkerscan be used for assessing a bladder cancer. The markers can beassociated with a vesicle or vesicle population.

Irritable Bowel Disease (IBD)

IBD versus normal biomarkers from a vesicle can include one or more (forexample, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 8,and can be used to create a IBD versus normal specific biosignature. Forexample, the one or more mRNAs that may be analyzed can include, but arenot limited to, REG1A, MMP3, or any combination thereof.

The invention also provides an isolated vesicle comprising one or morespecific biomarkers for distinguishing between IBD and a normal sample,such as listed in FIG. 8. A composition comprising the isolated vesicleis also provided. Accordingly, in some embodiments, the compositioncomprises a population of vesicles comprising one or more specificbiomarkers for distinguishing between IBD and a normal sample, such aslisted in FIG. 8. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for having one or more specific biomarkers fordistinguishing between IBD and a normal sample, such as listed in FIG.8.

One or more specific biomarkers for distinguishing between IBD and anormal sample, such as listed in FIG. 8 can also be detected by one ormore systems disclosed herein, for distinguishing between IBD and anormal sample. For example, a detection system can comprise one or moreprobes to detect one or more specific biomarkers for distinguishingbetween IBD and a normal sample, such as listed in FIG. 8, of one ormore vesicles of a biological sample.

Adenoma Versus Colorectal Cancer (CRC)

Adenoma versus CRC specific biomarkers from a vesicle can include one ormore (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 9,and can be used to create a Adenoma versus CRC specific biosignature.For example, the one or more mRNAs that may be analyzed can include, butare not limited to, GREM1, DDR2, GUCY1A3, TNS1, ADAMTS1, FBLN1,FLJ38028, RDX, FAM129A, ASPN, FRMD6, MCC, RBMS1, SNAI2, MEIS1, DOCK10,PLEKHC1, FAM126A, TBC1D9, VWF, DCN, ROBO1, MSRB3, LATS2, MEF2C, IGFBP3,GNB4, RCN3, AKAP12, RFTN1, 226834_at, COL5A1, GNG2, NR3C1*, SPARCL1,MAB21L2, AXIN2, 236894_at, AEBP1, AP1S2, C10orf56, LPHN2, AKT3, FRMD6,COL15A1, CRYAB, COL14A1, LOC286167, QKI, WWTR1, GNG11, PAPPA, or ELDT1,or any combination thereof.

The invention also provides an isolated vesicle comprising one or morespecific biomarkers for distinguishing between an adenoma and a CRC,such as listed in FIG. 9. A composition comprising the isolated vesicleis also provided. Accordingly, in some embodiments, the compositioncomprises a population of vesicles comprising one or more specificbiomarkers for distinguishing between an adenoma and a CRC, such aslisted in FIG. 9. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for having one or more specific biomarkers fordistinguishing between an adenoma and a CRC, such as listed in FIG. 9.

One or more specific biomarkers for distinguishing between an adenomaand a CRC, such as listed in FIG. 9 can also be detected by one or moresystems disclosed herein, for distinguishing between an adenoma and aCRC. For example, a detection system can comprise one or more probes todetect one or more specific biomarkers for distinguishing between anadenoma and a CRC, such as listed in FIG. 9, of one or more vesicles ofa biological sample.

IBD Versus CRC

IBD versus CRC specific biomarkers from a vesicle can include one ormore (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 10,and can be used to create a IBD versus CRC specific biosignature. Forexample, the one or more mRNAs that may be analyzed can include, but arenot limited to, 227458_at, INDO, CXCL9, CCR2, CD38, RARRES3, CXCL10,FAM26F, TNIP3, NOS2A, CCRL1, TLR8, IL18BP, FCRL5, SAMD9L, ECGF1,TNFSF13B, GBP5, or GBP1, or any combination thereof.

The invention also provides an isolated vesicle comprising one or morespecific biomarkers for distinguishing between IBD and a CRC, such aslisted in FIG. 10. A composition comprising the isolated vesicle is alsoprovided. Accordingly, in some embodiments, the composition comprises apopulation of vesicles comprising one or more specific biomarkers fordistinguishing between IBD and a CRC, such as listed in FIG. 10. Thecomposition can comprise a substantially enriched population ofvesicles, wherein the population of vesicles is substantiallyhomogeneous for having one or more specific biomarkers fordistinguishing between IBD and a CRC, such as listed in FIG. 10.

One or more specific biomarkers for distinguishing between IBD and aCRC, such as listed in FIG. 10 can also be detected by one or moresystems disclosed herein, for distinguishing between IBD and a CRC. Forexample, a detection system can comprise one or more probes to detectone or more specific biomarkers for distinguishing between IBD and aCRC, such as listed in FIG. 10, of one or more vesicles of a biologicalsample.

CRC Dukes B Versus Dukes C-D

CRC Dukes B versus Dukes C-D specific biomarkers from a vesicle caninclude one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more)overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations,proteins, ligands, peptides, snoRNA, or any combination thereof, such aslisted in FIG. 11, and can be used to create a CRC D-B versus C-Dspecific biosignature. For example, the one or more mRNAs that may beanalyzed can include, but are not limited to, TMEM37*, IL33, CA4,CCDC58, CLIC6, VERSUSNL1, ESPN, APCDD1, C13orf18, CYP4X1, ATP2A3,LOC646627, MUPCDH, ANPEP, C1orf115, HSD3B2, GBA3, GABRB2, GYLTL1B, LYZ,SPC25, CDKN2B, FAM89A, MOGAT2, SEMA6D, 229376_at, TSPAN5, IL6R, orSLC26A2, or any combination thereof.

The invention also provides an isolated vesicle comprising one or morespecific biomarkers for distinguishing between CRC Dukes B and a CRCDukes C-D, such as listed in FIG. 11. A composition comprising theisolated vesicle is also provided. Accordingly, in some embodiments, thecomposition comprises a population of vesicles comprising one or morespecific biomarkers for distinguishing between CRC Dukes B and a CRCDukes C-D, such as listed in FIG. 11. The composition can comprise asubstantially enriched population of vesicles, wherein the population ofvesicles is substantially homogeneous for having one or more specificbiomarkers for distinguishing between CRC Dukes B and a CRC Dukes C-D,such as listed in FIG. 11.

One or more specific biomarkers for distinguishing between CRC Dukes Band a CRC Dukes C-D, such as listed in FIG. 11 can also be detected byone or more systems disclosed herein, for distinguishing between CRCDukes B and a CRC Dukes C-D. For example, a detection system cancomprise one or more probes to detect one or more specific biomarkersfor distinguishing between CRC Dukes B and a CRC Dukes C-D, such aslisted in FIG. 11, of one or more vesicles of a biological sample.

Adenoma with Low Grade Dysplasia Versus Adenoma with High GradeDysplasia

Adenoma with low grade dysplasia versus adenoma with high gradedysplasia specific biomarkers from a vesicle can include one or more(for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 12,and can be used to create an adenoma low grade dysplasia versus adenomahigh grade dysplasia specific biosignature. For example, the one ormRNAs that may be analyzed can include, but are not limited to, SI,DMBT1, CFI*, AQP1, APOD, TNFRSF17, CXCL10, CTSE, IGHA1, SLC9A3, SLC7A1,BATF2, SOCS1, DOCK2, NOS2A, HK2, CXCL2, IL15RA, POU2AF1, CLEC3B, ANI3BP,MGC13057, LCK*, C4BPA, HOXC6, GOLT1A, C2orf32, IL10RA, 240856_at, SOC3,MEIS3P1, HIPK1, GLS, CPLX1, 236045_x_at, GALC, AMN, CCDC69, CCL28, CPA3,TRIB2, HMGA2, PLCL2, NR3C1, EIF5A, LARP4, RP5-1022P6.2, PHLDB2, FKBP1B,INDO, CLDN8, CNTN3, PBEF1, SLC16A9, CDC25B, TPSB2, PBEF1, ID4, GJB5,CHN2, LIMCH1, or CXCL9, or any combination thereof.

The invention also provides an isolated vesicle comprising one or morespecific biomarkers for distinguishing between adenoma with low gradedysplasia and adenoma with high grade dysplasia, such as listed in FIG.12. A composition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more specific biomarkers fordistinguishing between adenoma with low grade dysplasia and adenoma withhigh grade dysplasia, such as listed in FIG. 12. The composition cancomprise a substantially enriched population of vesicles, wherein thepopulation of vesicles is substantially homogeneous for having one ormore specific biomarkers for distinguishing between adenoma with lowgrade dysplasia and adenoma with high grade dysplasia, such as listed inFIG. 12.

One or more specific biomarkers for distinguishing between adenoma withlow grade dysplasia and adenoma with high grade dysplasia, such aslisted in FIG. 12 can also be detected by one or more systems disclosedherein, for distinguishing between adenoma with low grade dysplasia andadenoma with high grade dysplasia. For example, a detection system cancomprise one or more probes to detect one or more specific biomarkersfor distinguishing between adenoma with low grade dysplasia and adenomawith high grade dysplasia, such as listed in FIG. 12, of one or morevesicles of a biological sample.

Ulcerative Colitis (UC) Versus Crohn's Disease (CD)

Ulcerative colitis (UC) versus Crohn's disease (CD) specific biomarkersfrom a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7,8, or more) overexpressed miRs, underexpressed miRs, mRNAs, geneticmutations, proteins, ligands, peptides, snoRNA, or any combinationthereof, such as listed in FIG. 13, and can be used to create a UCversus CD specific biosignature. For example, the one or more mRNAs thatmay be analyzed can include, but are not limited to, IFITM1, IFITM3,STAT1, STAT3, TAP1, PSME2, PSMB8, HNF4G, KLF5, AQP8, APT2B1, SLC16A,MFAP4, CCNG2, SLC44A4, DDAH1, TOB1, 231152_at, MKNK1, CEACAM7*,1562836_at, CDC42SE2, PSD3, 231169_at, IGL@*, GSN, GPM6B, CDV3*, PDPK1,ANP32E, ADAM9, CDH1, NLRP2, 215777_at, OSBPL1, VNN1, RABGAP1L, PHACTR2,ASH1L, 213710_s_at, CDH1, NLRP2, 215777_at, OSBPL1, VNN1, RABGAP1L,PHACTR2, ASH1, 213710_s_at, ZNF3, FUT2, IGHA1, EDEM1, GPR171, 229713_at,LOC643187, FLVCR1, SNAP23*, ETNK1, LOC728411, POSTN, MUC12, HOXA5,SIGLEC1, LARP5, PIGR, SPTBN1, UFM1, C6orf62, WDR90, ALDH1A3, F2RL1,IGHV1-69, DUOX2, RAB5A, or CP, or any combination thereof can also beused as specific biomarkers from a vesicle for UC versus CD.

A biomarker mutation for distinguishing UC versus CD that can beassessed in a vesicle includes, but is not limited to, a mutation ofCARD15, or any combination of mutations specific for distinguishing UCversus CD. The protein, ligand, or peptide that can be assessed in avesicle can include, but is not limited to, (P) ASCA.

The invention also provides an isolated vesicle comprising one or morespecific biomarkers for distinguishing between UC and CD, such as listedin FIG. 13. A composition comprising the isolated vesicle is alsoprovided. Accordingly, in some embodiments, the composition comprises apopulation of vesicles comprising one or more specific biomarkers fordistinguishing between UC and CD, such as listed in FIG. 13. Thecomposition can comprise a substantially enriched population ofvesicles, wherein the population of vesicles is substantiallyhomogeneous for having one or more specific biomarkers fordistinguishing between UC and CD, such as listed in FIG. 13.

One or more specific biomarkers for distinguishing between UC and CD,such as listed in FIG. 13 can also be detected by one or more systemsdisclosed herein, for distinguishing between UC and CD. For example, adetection system can comprise one or more probes to detect one or morespecific biomarkers for distinguishing between UC and CD, such as listedin FIG. 13, of one or more vesicles of a biological sample.

Hyperplastic Polyp

Hyperplastic polyp versus normal specific biomarkers from a vesicle caninclude one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more)overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations,proteins, ligands, peptides, snoRNA, or any combination thereof, such aslisted in FIG. 14, and can be used to create a hyperplastic polyp versusnormal specific biosignature. For example, the one or more mRNAs thatmay be analyzed can include, but are not limited to, SLC6A14, ARHGEF10,ALS2, IL1RN, SPRY4, PTGER3, TRIM29, SERPINB5, 1560327_(—)4 ZAK, BAG4,TRIB3, TTL, FOXQ1, or any combination.

The invention also provides an isolated vesicle comprising one or morehyperplastic polyp specific biomarkers, such as listed in FIG. 14. Acomposition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more hyperplastic polyp specificbiomarkers, such as listed in FIG. 14. The composition can comprise asubstantially enriched population of vesicles, wherein the population ofvesicles is substantially homogeneous for hyperplastic polyp specificvesicles or vesicles comprising one or more hyperplastic polyp specificbiomarkers, such as listed in FIG. 14.

One or more hyperplastic polyp specific biomarkers, such as listed inFIG. 14 can also be detected by one or more systems disclosed herein,for characterizing a hyperplastic polyp. For example, a detection systemcan comprise one or more probes to detect one or more listed in FIG. 14.One or more hyperplastic specific biomarkers, such as listed in FIG. 14,of one or more vesicles of a biological sample.

Adenoma with Low Grade Dysplasia Versus Normal

Adenoma with low grade dysplasia versus normal specific biomarkers froma vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, ormore) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations,proteins, ligands, peptides, snoRNA, or any combination thereof, such aslisted in FIG. 15, and can be used to create an adenoma low gradedysplasia versus normal specific biosignature. For example, the RNAsthat may be analyzed can include, but are not limited to, UGT2A3, KLK11,KIAA1199, FOXQ1, CLDN8, ABCA8, or PYY, or any combination thereof andcan be used as specific biomarkers from a vesicle for Adenoma low gradedysplasia versus normal. Furthermore, the snoRNA that can be used as anexosomal biomarker for adenoma low grade dysplasia versus normal caninclude, but is not limited to, GAS5.

The invention also provides an isolated vesicle comprising one or morespecific biomarkers for distinguishing between adenoma with low gradedysplasia and normal, such as listed in FIG. 15. A compositioncomprising the isolated vesicle is also provided. Accordingly, in someembodiments, the composition comprises a population of vesiclescomprising one or more specific biomarkers for distinguishing betweenadenoma with low grade dysplasia and normal, such as listed in FIG. 15.The composition can comprise a substantially enriched population ofvesicles, wherein the population of vesicles is substantiallyhomogeneous for having one or more specific biomarkers fordistinguishing between adenoma with low grade dysplasia and normal, suchas listed in FIG. 15.

One or more specific biomarkers for distinguishing between adenoma withlow grade dysplasia and normal, such as listed in FIG. 15 can also bedetected by one or more systems disclosed herein, for distinguishingbetween adenoma with low grade dysplasia and normal. For example, adetection system can comprise one or more probes to detect one or morespecific biomarkers for distinguishing between adenoma with low gradedysplasia and normal, such as listed in FIG. 15, of one or more vesiclesof a biological sample.

Adenoma Versus Normal

Adenoma versus normal specific biomarkers from a vesicle can include oneor more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 16,and can be used to create an Adenoma versus normal specificbiosignature. For example, the one or more mRNAs that may be analyzedcan include, but are not limited to, KIAA1199, FOXQ1, or CA7, or anycombination thereof. The protein, ligand, or peptide that can be used asa biomarker from a vesicle that is specific to adenoma versus. normalcan include, but is not limited to, Clusterin.

The invention also provides an isolated vesicle comprising one or morespecific biomarkers for distinguishing between adenoma and normal, suchas listed in FIG. 16. A composition comprising the isolated vesicle isalso provided. Accordingly, in some embodiments, the compositioncomprises a population of vesicles comprising one or more specificbiomarkers for distinguishing between adenoma and normal, such as listedin FIG. 16. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for having one or more specific biomarkers fordistinguishing between adenoma and normal, such as listed in FIG. 16.

One or more specific biomarkers for distinguishing between adenoma andnormal, such as listed in FIG. 16 can also be detected by one or moresystems disclosed herein, for distinguishing between adenoma and normal.For example, a detection system can comprise one or more probes todetect one or more specific biomarkers for distinguishing betweenadenoma and normal, such as listed in FIG. 16, of one or more vesiclesof a biological sample.

CRC Versus Normal

CRC versus normal specific biomarkers from a vesicle can include one ormore (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 17,and can be used to create a CRC versus normal specific biosignature. Forexample, the one or mRNAs that may be analyzed can include, but are notlimited to, VWF, IL8, CHI3L1, S100A8, GREM1, or ODC, or any combinationthereof and can be used as specific biomarkers from a vesicle for CRCversus normal.

A biomarker mutation for CRC versus normal that can be assessed in avesicle includes, but is not limited to, a mutation of KRAS, BRAF, APC,MSH2, or MLH1, or any combination of mutations specific fordistinguishing between CRC versus normal. The protein, ligand, orpeptide that can be assessed in a vesicle can include, but is notlimited to, cytokeratin 13, calcineurin, CHK1, clathrin light chain,phospho-ERK, phospho-PTK2, or MDM2, or any combination thereof.

The invention also provides an isolated vesicle comprising one or morespecific biomarkers for distinguishing between CRC and normal, such aslisted in FIG. 17. A composition comprising the isolated vesicle is alsoprovided. Accordingly, in some embodiments, the composition comprises apopulation of vesicles comprising one or more specific biomarkers fordistinguishing between CRC and normal, such as listed in FIG. 17. Thecomposition can comprise a substantially enriched population ofvesicles, wherein the population of vesicles is substantiallyhomogeneous for having one or more specific biomarkers fordistinguishing between CRC and normal, such as listed in FIG. 17.

One or more specific biomarkers for distinguishing between CRC andnormal, such as listed in FIG. 17 can also be detected by one or moresystems disclosed herein, for distinguishing between CRC and normal. Forexample, a detection system can comprise one or more probes to detectone or more specific biomarkers for distinguishing between CRC andnormal, such as listed in FIG. 17, of one or more vesicles of abiological sample.

Benign Prostatic Hyperplasia (BPH)

Benign prostatic hyperplasia (BPH) specific biomarkers from a vesiclecan include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more)overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations,proteins, ligands, peptides, snoRNA, or any combination thereof, such aslisted in FIG. 18, and can be used to create a BPH specificbiosignature. The protein, ligand, or peptide that can be assessed in avesicle can include, but is not limited to, intact fibronectin.

The invention also provides an isolated vesicle comprising one or moreBPH specific biomarkers, such as listed in FIG. 18 and in FIG. 1 forBPH. A composition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more BPH specific biomarkers, such aslisted in FIG. 18 and in FIG. 1 for BPH. The composition can comprise asubstantially enriched population of vesicles, wherein the population ofvesicles is substantially homogeneous for BPH specific vesicles orvesicles comprising one or more BPH specific biomarkers, such as listedin FIG. 18 and in FIG. 1 for BPH.

One or more BPH specific biomarkers, such as listed in FIG. 18 and inFIG. 1 for BPH, can also be detected by one or more systems disclosedherein, for characterizing a BPH. For example, a detection system cancomprise one or more probes to detect one or more BPH specificbiomarkers, such as listed in FIG. 18 and in FIG. 1 for BPH, of one ormore vesicles of a biological sample.

Prostate Cancer

Prostate cancer specific biomarkers from a vesicle can include one ormore (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 19,and can be used to create a prostate cancer specific biosignature. Forexample, a biosignature for prostate cancer can comprise miR-9, miR-21,miR-141, miR-370, miR-200b, miR-210, miR-155, or miR-196a. In someembodiments, the biosignature can comprise one or more overexpressedmiRs, such as, but not limited to, miR-202, miR-210, miR-296, miR-320,miR-370, miR-373, miR-498, miR-503, miR-184, miR-198, miR-302c, miR-345,miR-491, miR-513, miR-32, miR-182, miR-31, miR-26a-1/2, miR-200c,miR-375, miR-196a-1/2, miR-370, miR-425, miR-425, miR-194-1/2,miR-181a-1/2, miR-34b, let-7i, miR-188, miR-25, miR-106b, miR-449,miR-99b, miR-93, miR-92-1/2, miR-125a, or miR-141, or any combinationthereof.

The biosignature can also comprise one or more underexpressed miRs suchas, but not limited to, let-7a, let-7b, let-7c, let-7d, let-7g, miR-16,miR-23a, miR-23b, miR-26a, miR-92, miR-99a, miR-103, miR-125a, miR-125b,miR-143, miR-145, miR-195, miR-199, miR-221, miR-222, miR-497, let-7f,miR-19b, miR-22, miR-26b, miR-27a, miR-27b, miR-29a, miR-29b, miR-305p,miR-30c, miR-100, miR-141, miR-148a, miR-205, miR-520h, miR-494,miR-490, miR-133a-1, miR-1-2, miR-218-2, miR-220, miR-128a, miR-221,miR-499, miR-329, miR-340, miR-345, miR-410, miR-126, miR-205,miR-7-1/2, miR-145, miR-34a, miR-487, or let-7b, or any combinationthereof. The biosignature can comprise upregulated or overexpressedmiR-21, downregulated or underexpressed miR-15a, miR-16-1, miR-143 ormiR-145, or any combination there.

The one or more mRNAs that may be analyzed can include, but are notlimited to, AR, PCA3, or any combination thereof and can be used asspecific biomarkers from a vesicle for prostate cancer.

The protein, ligand, or peptide that can be assessed in a vesicle caninclude, but is not limited to, FASLG or TNFSF10 or any combinationthereof. Furthermore, a vesicle isolated or assayed can be prostatecancer cell specific, or derived from prostate cancer cells.Furthermore, the snoRNA that can be used as an exosomal biomarker forprostate cancer can include, but is not limited to, U50. Examples ofprostate cancer biosignatures are further described below.

The invention also provides an isolated vesicle comprising one or moreprostate cancer specific biomarkers, such as ACSL3-ETV1, C15ORF21-ETV1,FLJ35294-ETV1, HERV-ETV1, TMPRSS2-ERG, TMPRSS2-ETV1/4/5, TMPRSS2-ETV4/5,SLC5A3-ERG, SLC5A3-ETV1, SLC5A3-ETV5 or KLK2-ETV4, or those listed inFIGS. 19, 60 and in FIG. 1 for prostate cancer. In some embodiments, theisolated vesicle is EpCam+, CK+, CD45−. A composition comprising theisolated vesicle is also provided. Accordingly, in some embodiments, thecomposition comprises a population of vesicles comprising one or moreprostate cancer specific biomarkers such as ACSL3-ETV1, C15ORF21-ETV1,FLJ35294-ETV1, HERV-ETV1, TMPRSS2-ERG, TMPRSS2-ETV1/4/5, TMPRSS2-ETV4/5,SLC5A3-ERG, SLC5A3-ETV1, SLC5A3-ETV5 or KLK2-ETV4, or those listed inFIGS. 19, 60 and in FIG. 1 for prostate cancer. In some embodiments, thecomposition comprises a population of vesicles that are EpCam+, CK+,CD45−. The composition can comprise a substantially enriched populationof vesicles, wherein the population of vesicles is substantiallyhomogeneous for prostate cancer specific vesicles or vesicles comprisingone or more prostate cancer specific biomarkers, such as ACSL3-ETV1,C15ORF21-ETV1, FLJ35294-ETV1, HERV-ETV1, TMPRSS2-ERG, TMPRSS2-ETV1/4/5,TMPRSS2-ETV4/5, SLC5A3-ERG, SLC5A3-ETV1, SLC5A3-ETV5 or KLK2-ETV4, orthose listed in FIGS. 19, 60 and in FIG. 1 for prostate cancer. In oneembodiment, the composition can comprise a substantially enrichedpopulation of vesicles that are EpCam+, CK+, CD45−.

One or more prostate cancer specific biomarkers, such as ACSL3-ETV1,C15ORF21-ETV1, FLJ35294-ETV1, HERV-ETV1, TMPRSS2-ERG, TMPRSS2-ETV1/4/5,TMPRSS2-ETV4/5, SLC5A3-ERG, SLC5A3-ETV1, SLC5A3-ETV5 or KLK2-ETV4, orthose listed in FIGS. 19, 60 and in FIG. 1 for prostate cancer can alsobe detected by one or more systems disclosed herein, for characterizinga prostate cancer. In some embodiments, the biomarkers EpCam, CK(cytokeratin), and CD45 are detected by one or more of systems disclosedherein, for characterizing prostate cancer, such as determining theprognosis for a subject's prostate cancer, or the therapy-resistance ofa subject. For example, a detection system can comprise one or moreprobes to detect one or more prostate cancer specific biomarkers, suchas ACSL3-ETV1, C15ORF21-ETV1, FLJ35294-ETV1, HERV-ETV1, TMPRSS2-ERG,TMPRSS2-ETV1/4/5, TMPRSS2-ETV4/5, SLC5A3-ERG, SLC5A3-ETV1, SLC5A3-ETV5or KLK2-ETV4, or those listed in FIGS. 19, 60 and in FIG. 1 for prostatecancer, of one or more vesicles of a biological sample. In oneembodiment, the detection system can comprise one or more probes todetect EpCam, CK, CD45, or a combination thereof.

Melanoma

Melanoma specific biomarkers from a vesicle can include one or more (forexample, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 20,and can be used to create a melanoma specific biosignature. For example,the biosignature can comprise one or more overexpressed miRs, such as,but not limited to, miR-19a, miR-144, miR-200c, miR-211, miR-324-5p,miR-331, or miR-374, or any combination thereof. The biosignature canalso comprise one or more underexpressed miRs such as, but not limitedto, miR-9, miR-15a, miR-17-3p, miR-23b, miR-27a, miR-28, miR-29b,miR-30b, miR-31, miR-34b, miR-34c, miR-95, miR-96, miR-100, miR-104,miR-105, miR-106a, miR-107, miR-122a, miR-124a, miR-125b, miR-127,miR-128a, miR-128b, miR-129, miR-135a, miR-135b, miR-137, miR-138,miR-139, miR-140, miR-141, miR-149, miR-154, miR-154#3, miR-181a,miR-182, miR-183, miR-184, miR-185, miR-189, miR-190, miR-199, miR-199b,miR-200a, miR-200b, miR-204, miR-213, miR-215, miR-216, miR-219,miR-222, miR-224, miR-299, miR-302a, miR-302b, miR-302c, miR-302d,miR-323, miR-325, let-7a, let-7b, let-7d, let-7e, or let-7g, or anycombination thereof.

The one or more mRNAs that may be analyzed can include, but are notlimited to, MUM-1, beta-catenin, or Nop/5/Sik, or any combinationthereof and can be used as specific biomarkers from a vesicle formelanoma.

A biomarker mutation for melanoma that can be assessed in a vesicleincludes, but is not limited to, a mutation of CDK4 or any combinationof mutations specific for melanoma. The protein, ligand, or peptide thatcan be assessed in a vesicle can include, but is not limited to, DUSP-1,Alix, hsp70, Gib2, Gia, moesin, GAPDH, malate dehydrogenase, p120catenin, PGRL, syntaxin-binding protein 1 & 2, septin-2, or WD-repeatcontaining protein 1, or any combination thereof. The snoRNA that can beused as an exosomal biomarker for melanoma include, but are not limitedto, H/ACA (U107f), SNORA11D, or any combination thereof. Furthermore, avesicle isolated or assayed can be melanoma cell specific, or derivedfrom melanoma cells.

The invention also provides an isolated vesicle comprising one or moremelanoma specific biomarkers, such as listed in FIG. 20 and in FIG. 1for melanoma. A composition comprising the isolated vesicle is alsoprovided. Accordingly, in some embodiments, the composition comprises apopulation of vesicles comprising one or more melanoma specificbiomarkers, such as listed in FIG. 20 and in FIG. 1 for melanoma. Thecomposition can comprise a substantially enriched population ofvesicles, wherein the population of vesicles is substantiallyhomogeneous for melanoma specific vesicles or vesicles comprising one ormore melanoma specific biomarkers, such as listed in FIG. 20 and in FIG.1 for melanoma.

One or more melanoma specific biomarkers, such as listed in FIG. 20 andin FIG. 1 for melanoma can also be detected by one or more systemsdisclosed herein, for characterizing a melanoma. For example, adetection system can comprise one or more probes to detect one or morecancer specific biomarkers, such as listed in FIG. 20 and in FIG. 1 formelanoma, of one or more vesicles of a biological sample.

Pancreatic Cancer

Pancreatic cancer specific biomarkers from a vesicle can include one ormore (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 21,and can be used to create a pancreatic cancer specific biosignature. Forexample, the biosignature can comprise one or more overexpressed miRs,such as, but not limited to, miR-221, miR-181a, miR-155, miR-210,miR-213, miR-181b, miR-222, miR-181b-2, miR-21, miR-181b-1, miR-220,miR-181d, miR-223, miR-100-1/2, miR-125a, miR-143, miR-10a, miR-146,miR-99, miR-100, miR-199a-1, miR-10b, miR-199a-2, miR-221, miR-181a,miR-155, miR-210, miR-213, miR-181b, miR-222, miR-181b-2, miR-21,miR-181b-1, miR-181c, miR-220, miR-181d, miR-223, miR-100-1/2, miR-125a,miR-143, miR-10a, miR-146, miR-99, miR-100, miR-199a-1, miR-10b,miR-199a-2, miR-107, miR-103, miR-103-2, miR-125b-1, miR-205, miR-23a,miR-221, miR-424, miR-301, miR-100, miR-376a, miR-125b-1, miR-21,miR-16-1, miR-181a, miR-181c, miR-92, miR-15, miR-155, let-7f-1,miR-212, miR-107, miR-024-1/2, miR-18a, miR-31, miR-93, miR-224, orlet-7d, or any combination thereof.

The biosignature can also comprise one or more underexpressed miRs suchas, but not limited to, miR-148a, miR-148b, miR-375, miR-345, miR-142,miR-133a, miR-216, miR-217 or miR-139, or any combination thereof. Theone or more mRNAs that may be analyzed can include, but are not limitedto, PSCA, Mesothelin, or Osteopontin, or any combination thereof and canbe used as specific biomarkers from a vesicle for pancreatic cancer.

A biomarker mutation for pancreatic cancer that can be assessed in avesicle includes, but is not limited to, a mutation of KRAS, CTNNLB1,AKT, NCOA3, or B-RAF, or any combination of mutations specific forpancreatic cancer. The biomarker can also be BRCA2, PALB2, or p16.Furthermore, a vesicle isolated or assayed can be pancreatic cancer cellspecific, or derived from pancreatic cancer cells.

The invention also provides an isolated vesicle comprising one or morepancreatic cancer specific biomarkers, such as listed in FIG. 21. Acomposition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more pancreatic cancer specificbiomarkers, such as listed in FIG. 21. The composition can comprise asubstantially enriched population of vesicles, wherein the population ofvesicles is substantially homogeneous for pancreatic cancer specificvesicles or vesicles comprising one or more pancreatic cancer specificbiomarkers, such as listed in FIG. 21.

One or more pancreatic cancer specific biomarkers, such as listed inFIG. 21, can also be detected by one or more systems disclosed herein,for characterizing a pancreatic cancer. For example, a detection systemcan comprise one or more probes to detect one or more pancreatic cancerspecific biomarkers, such as listed in FIG. 21, of one or more vesiclesof a biological sample.

Brain Cancer

Brain cancer (including, but not limited to, gliomas, glioblastomas,meinigiomas, acoustic neuroma/schwannomas, medulloblastoma) specificbiomarkers from a vesicle can include one or more (for example, 2, 3, 4,5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs,genetic mutations, proteins, ligands, peptides, snoRNA, or anycombination thereof, such as listed in FIG. 22, and can be used tocreate a brain cancer specific biosignature. For example, thebiosignature can comprise one or more overexpressed miRs, such as, butnot limited to miR-21, miR-10b, miR-130a, miR-221, miR-125b-1,miR-125b-2, miR-9-2, miR-21, miR-25, or miR-123, or any combinationthereof.

The biosignature can also comprise one or more underexpressed miRs suchas, but not limited to, miR-128a, miR-181c, miR-181a, or miR-181b, orany combination thereof. The one or more mRNAs that may be analyzedinclude, but are not limited to, MGMT, which can be used as specificbiomarker from a vesicle for brain cancer. The protein, ligand, orpeptide that can be assessed in a vesicle can include, but is notlimited to, EGFR.

The invention also provides an isolated vesicle comprising one or morebrain cancer specific biomarkers, such as GOPC-ROS1, or those listed inFIG. 22 and in FIG. 1 for brain cancer. A composition comprising theisolated vesicle is also provided. Accordingly, in some embodiments, thecomposition comprises a population of vesicles comprising one or morebrain cancer specific biomarkers, such as GOPC-ROS1, or those listed inFIG. 22 and in FIG. 1 for brain cancer. The composition can comprise asubstantially enriched population of vesicles, wherein the population ofvesicles is substantially homogeneous for brain cancer specific vesiclesor vesicles comprising one or more brain cancer specific biomarkers,such as GOPC-ROS1, or those listed in FIG. 22 and in FIG. 1 for braincancer.

One or more brain cancer specific biomarkers, such as listed in FIG. 22and in FIG. 1 for brain cancer, can also be detected by one or moresystems disclosed herein, for characterizing a brain cancer. Forexample, a detection system can comprise one or more probes to detectone or more brain cancer specific biomarkers, such as GOPC-ROS1, orthose listed in FIG. 22 and in FIG. 1 for brain cancer, of one or morevesicles of a biological sample.

Psoriasis

Psoriasis specific biomarkers from a vesicle can include one or more(for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 23,and can be used to create a psoriasis specific biosignature. Forexample, the biosignature can comprise one or more overexpressed miRs,such as, but not limited to, miR-146b, miR-20a, miR-146a, miR-31,miR-200a, miR-17-5p, miR-30e-5p, miR-141, miR-203, miR-142-3p, miR-21,or miR-106a, or any combination thereof. The biosignature can alsocomprise one or more underexpressed miRs such a, but not limited to,miR-125b, miR-99b, miR-122a, miR-197, miR-100, miR-381, miR-518b,miR-524, let-7e, miR-30c, miR-365, miR-133b, miR-10a, miR-133a, miR-22,miR-326, or miR-215, or any combination thereof.

The oneor more mRNAs that may be analyzed can include, but are notlimited to, IL-20, VEGFR-1, VEGFR-2, VEGFR-3, or EGR1, or anycombination thereof and can be used as specific biomarkers from avesicle for psoriasis. A biomarker mutation for psoriasis that can beassessed in a vesicle includes, but is not limited to, a mutation ofMGST2, or any combination of mutations specific for psoriasis.

The invention also provides an isolated vesicle comprising one or morepsoriasis specific biomarkers, such as listed in FIG. 23 and in FIG. 1for psoriasis. A composition comprising the isolated vesicle is alsoprovided. Accordingly, in some embodiments, the composition comprises apopulation of vesicles comprising one or more psoriasis specificbiomarkers, such as listed in FIG. 23 and in FIG. 1 for psoriasis. Thecomposition can comprise a substantially enriched population ofvesicles, wherein the population of vesicles is substantiallyhomogeneous for psoriasis specific vesicles or vesicles comprising oneor more psoriasis specific biomarkers, such as listed in FIG. 23 and inFIG. 1 for psoriasis.

One or more psoriasis specific biomarkers, such as listed in FIG. 23 andin FIG. 1 for psoriasis, can also be detected by one or more systemsdisclosed herein, for characterizing psoriasis. For example, a detectionsystem can comprise one or more probes to detect one or more psoriasisspecific biomarkers, such as listed in FIG. 23 and in FIG. 1 forpsoriasis, of one or more vesicles of a biological sample.

Cardiovascular Disease (CVD)

CVD specific biomarkers from a vesicle can include one or more (forexample, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 24,and can be used to create a CVD specific biosignature. For example, thebiosignature can comprise one or more overexpressed miRs, such as, butnot limited to, miR-195, miR-208, miR-214, let-7b, let-7c, let-7e,miR-15b, miR-23a, miR-24, miR-27a, miR-27b, miR-93, miR-99b, miR-100,miR-103, miR-125b, miR-140, miR-145, miR-181a, miR-191, miR-195,miR-199a, miR-320, miR-342, miR-451, or miR-499, or any combinationthereof.

The biosignature can also comprise one or more underexpressed miRs suchas, but not limited to, miR-1, miR-10a, miR-17-5p, miR-19a, miR-19b,miR-20a, miR-20b, miR-26b, miR-28, miR-30e-5p, miR-101, miR-106a,miR-126, miR-222, miR-374, miR-422b, or miR-423, or any combinationthereof. The mRNAs that may be analyzed can include, but are not limitedto, MRP14, CD69, or any combination thereof and can be used as specificbiomarkers from a vesicle for CVD.

A biomarker mutation for CVD that can be assessed in a vesicle includes,but is not limited to, a mutation of MYH7, SCN5A, or CHRM2, or anycombination of mutations specific for CVD.

The protein, ligand, or peptide that can be assessed in a vesicle caninclude, but is not limited to, CK-MB, cTnI (cardiac troponin), CRP,BPN, IL-6, MCSF, CD40, CD40L, or any combination thereof. Furthermore, avesicle isolated or assayed can be a CVD cell specific, or derived fromcardiac cells.

The invention also provides an isolated vesicle comprising one or moreCVD specific biomarkers, such as listed in FIG. 24 and in FIG. 1 forCVD. A composition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more CVD specific biomarkers, such aslisted in FIG. 24 and in FIG. 1 for CVD. The composition can comprise asubstantially enriched population of vesicles, wherein the population ofvesicles is substantially homogeneous for CVD specific vesicles orvesicles comprising one or more CVD specific biomarkers, such as listedin FIG. 24 and in FIG. 1 for CVD.

One or more CVD specific biomarkers, such as listed in FIG. 24 and inFIG. 1 for CVD, can also be detected by one or more systems disclosedherein, for characterizing a CVD. For example, a detection system cancomprise one or more probes to detect one or more CVD specificbiomarkers, such as listed in FIG. 24 and in FIG. 1 for CVD, of one ormore vesicles of a biological sample.

An increase in an miRNA or combination or miRNA, such as miR-21,miR-129, miR-212, miR-214, miR-134, or a combination thereof (asdisclosed in US Publication No. 2010/0010073), can be used to diagnosean increased risk of development or already the existence of cardiachypertrophy and/or heart failure. A downregulation of miR-182, miR-290,or a combination thereof can be used to diagnose an increased risk ofdevelopment or already the existence of cardiac hypertrophy and/or heartfailure. An increased expression of miR-21, miR-129, miR-212, miR-214,miR-134, or a combination thereof with a reduced expression of miR-182,miR-290, or a combination thereof, may be used to diagnose an increasedrisk of development or the existence of cardiac hypertrophy and/or heartfailure.

Blood Cancers

Hematological malignancies specific biomarkers from a vesicle caninclude one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more)overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations,proteins, ligands, peptides, snoRNA, or any combination thereof, such aslisted in FIG. 25, and can be used to create a hematologicalmalignancies specific biosignature. For example, the one or more mRNAsthat may be analyzed can include, but are not limited to, HOX11, TAL1,LY1, LMO1, or LMO2, or any combination thereof and can be used asspecific biomarkers from a vesicle for hematological malignancies.

A biomarker mutation for a blood cancer that can be assessed in avesicle includes, but is not limited to, a mutation of c-kit, PDGFR, orABL, or any combination of mutations specific for hematologicalmalignancies.

The invention also provides an isolated vesicle comprising one or moreblood cancer specific biomarkers, such as listed in FIG. 25 and in FIG.1 for blood cancer. A composition comprising the isolated vesicle isalso provided. Accordingly, in some embodiments, the compositioncomprises a population of vesicles comprising one or more blood cancerspecific biomarkers, such as listed in FIG. 25 and in FIG. 1 for bloodcancer. The composition can comprise a substantially enriched populationof vesicles, wherein the population of vesicles is substantiallyhomogeneous for blood cancer specific vesicles or vesicles comprisingone or more blood cancer specific biomarkers, such as listed in FIG. 25and in FIG. 1 for blood cancer.

One or more blood cancer specific biomarkers, such as listed in FIG. 25and in FIG. 1 for blood cancer, can also be detected by one or moresystems disclosed herein, for characterizing a blood cancer. Forexample, a detection system can comprise one or more probes to detectone or more blood cancer specific biomarkers, such as listed in FIG. 25and in FIG. 1 for blood cancer, of one or more vesicles of a biologicalsample.

The one or more blood cancer specific biomarkers can also be a genefusion selected from the group consisting of: TTL-ETV6, CDK6-MLL,CDK6-TLX3, ETV6-FLT3, ETV6-RUNX1, ETV6-TTL, MLL-AFF1, MLL-AFF3,MLL-AFF4, MLL-GAS7, TCBA1-ETV6, TCF3-PBX1 or TCF3-TFPT, for acutelymphocytic leukemia (ALL); BCL11B-TLX3, IL2-TNFRFS17, NUP214-ABL1,NUP98-CCDC28A, TAL1-STIL, or ETV6-ABL2, for T-cell acute lymphocyticleukemia (T-ALL); ATIC-ALK, KIAA1618-ALK, MSN-ALK, MYH9-ALK, NPM1-ALK,TGF-ALK or TPM3-ALK, for anaplastic large cell lymphoma (ALCL);BCR-ABL1, BCR-JAK2, ETV6-EVI1, ETV6-MN1 or ETV6-TCBA1, for chronicmyelogenous leukemia (CML); CBFB-MYH11, CHIC2-ETV6, ETV6-ABL1,ETV6-ABL2, ETV6-ARNT, ETV6-CDX2, ETV6-HLXB9, ETV6-PER1, MEF2D-DAZAP1,AML-AFF1, MLL-ARHGAP26, MLL-ARHGEF12, MLL-CASC5, MLL-CBL, MLL-CREBBP,MLL-DAB21P, MLL-ELL, MLL-EP300, MLL-EPS15, MLL-FNBP1, MLL-FOXO3A,MLL-GMPS, MLL-GPHN, MLL-MLLT1, MLL-MLLT11, MLL-MLLT3, MLL-MLLT6,MLL-MYO1F, MLL-PICALM, MLL-SEPT2, MLL-SEPT6, MLL-SORBS2, MYST3-SORBS2,MYST-CREBBP, NPM1-MLF1, NUP98-HOXA13, PRDM16-EVI1, RABEP1-PDGFRB,RUNX1-EVI1, RUNX1-MDS1, RUNX1-RPL22, RUNX1-RUNX1T1, RUNX1-SH3D19,RUNX1-USP42, RUNX1-YTHDF2, RUNX1-ZNF687, or TAF15-ZNF-384, for AML;CCND1-FSTL3, for chronic lymphocytic leukemia (CLL); and FLIP1-PDGFRA,FLT3-ETV6, KIAA1509-PDGFRA, PDE4DIP-PDGFRB, NIN-PDGFRB, TP53BP1-PDGFRB,or TPM3-PDGFRB, for hyper eosinophilia/chronic eosinophilia.

The one or more biomarkers for CLL can also include one or more of thefollowing upregulated or overexpressed miRNAs, such as miR-23b,miR-24-1, miR-146, miR-155, miR-195, miR-221, miR-331, miR-29a, miR-195,miR-34a, or miR-29c; one or more of the following downregulated orunderexpressed miRs, such as miR-15a, miR-16-1, miR-29 or miR-223, orany combination thereof.

The one or more biomarkers for ALL can also include one or more of thefollowing upregulated or overexpressed miRNAs, such as miR-128b,miR-204, miR-218, miR-331, miR-181b-1, miR-17-92; or any combinationthereof.

B-Cell Chronic Lymphocytic Leukemia (B-CLL)

B-CLL specific biomarkers from a vesicle can include one or more (forexample, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 26,and can be used to create a B-CLL specific biosignature. For example,the biosignature can comprise one or more overexpressed miRs, such as,but not limited to, miR-183-prec, miR-190, miR-24-1-prec, miR-33,miR-19a, miR-140, miR-123, miR-10b, miR-15b-prec, miR-92-1, miR-188,miR-154, miR-217, miR-101, miR-141-prec, miR-153-prec, miR-196-2,miR-134, miR-141, miR-132, miR-192, or miR-181b-prec, or any combinationthereof.

The biosignature can also comprise one or more underexpressed miRs suchas, but not limited to, miR-213, miR-220, or any combination thereof.The one or more mRNAs that may be analyzed can include, but are notlimited to, ZAP70, AdipoR1, or any combination thereof and can be usedas specific biomarkers from a vesicle for B-CLL. A biomarker mutationfor B-CLL that can be assessed in a vesicle includes, but is not limitedto, a mutation of IGHV, P53, ATM, or any combination of mutationsspecific for B-CLL.

The invention also provides an isolated vesicle comprising one or moreB-CLL specific biomarkers, such as BCL3-MYC, MYC-BTG1, BCL7A-MYC,BRWD3-ARHGAP20 or BTG1-MYC, or those listed in FIG. 26. A compositioncomprising the isolated vesicle is also provided. Accordingly, in someembodiments, the composition comprises a population of vesiclescomprising one or more B-CLL specific biomarkers, such as BCL3-MYC,MYC-BTG1, BCL7A-MYC, BRWD3-ARHGAP20 or BTG1-MYC, or those listed in FIG.26. The composition can comprise a substantially enriched population ofvesicles, wherein the population of vesicles is substantiallyhomogeneous for B-CLL specific vesicles or vesicles comprising one ormore B-CLL specific biomarkers, such as BCL3-MYC, MYC-BTG1, BCL7A-MYC,BRWD3-ARHGAP20 or BTG1-MYC, or those listed in FIG. 26.

One or more B-CLL specific biomarkers, such as BCL3-MYC, MYC-BTG1,BCL7A-MYC, BRWD3-ARHGAP20 or BTG1-MYC, or those listed in FIG. 26, canalso be detected by one or more systems disclosed herein, forcharacterizing a B-CLL. For example, a detection system can comprise oneor more probes to detect one or more B-CLL specific biomarkers, such asBCL3-MYC, MYC-BTG1, BCL7A-MYC, BRWD3-ARHGAP20 or BTG1-MYC, or thoselisted in FIG. 26, of one or more vesicles of a biological sample.

B-Cell Lymphoma

B-cell lymphome specific biomarkers from a vesicle can include one ormore (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 27,and can be used to create a B-cell lymphoma specific biosignature. Forexample, the biosignature can comprise one or more overexpressed miRs,such as, but not limited to, miR-17-92 polycistron, miR-155, miR-210, ormiR-21, miR-19a, miR-92, miR-142 miR-155, miR-221 miR-17-92, miR-21,miR-191, miR-205, or any combination thereof. Furthermore the snoRNAthat can be used as an exosomal biomarker for B-cell lymphoma caninclude, but is not limited to, U50.

The invention also provides an isolated vesicle comprising one or moreB-cell lymphoma specific biomarkers, such as listed in FIG. 27. Acomposition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more B-cell lymphoma specific biomarkers,such as listed in FIG. 27. The composition can comprise a substantiallyenriched population of vesicles, wherein the population of vesicles issubstantially homogeneous for B-cell lymphoma specific vesicles orvesicles comprising one or more B-cell lymphoma specific biomarkers,such as listed in FIG. 27.

One or more B-cell lymphoma specific biomarkers, such as listed in FIG.27, can also be detected by one or more systems disclosed herein, forcharacterizing a B-cell lymphoma. For example, a detection system cancomprise one or more probes to detect one or more B-cell lymphomaspecific biomarkers, such as listed in FIG. 27, of one or more vesiclesof a biological sample.

Diffuse Large B-Cell Lymphoma (DLBCL)

DLBCL specific biomarkers from a vesicle can include one or more (forexample, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 28,and can be used to create a DLBCL specific biosignature. For example,the biosignature can comprise one or more overexpressed miRs, such as,but not limited to, miR-17-92, miR-155, miR-210, or miR-21, or anycombination thereof. The one or more mRNAs that may be analyzed caninclude, but are not limited to, A-myb, LMO2, JNK3, CD10, bcl-6, CyclinD2, IRF4, Flip, or CD44, or any combination thereof and can be used asspecific biomarkers from a vesicle for DLBCL.

The invention also provides an isolated vesicle comprising one or moreDLBCL specific biomarkers, such as CITTA-BCL6, CLTC-ALK, IL21R-BCL6,PIM1-BCL6, TFCR-BCL6, IKZF1-BCL6 or SEC31A-ALK, or those listed in FIG.28. A composition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more DLBCL specific biomarkers, such asCITTA-BCL6, CLTC-ALK, IL21R-BCL6, PIM1-BCL6, TFCR-BCL6, IKZF1-BCL6 orSEC31A-ALK, or those listed in FIG. 28. The composition can comprise asubstantially enriched population of vesicles, wherein the population ofvesicles is substantially homogeneous for DLBCL specific vesicles orvesicles comprising one or more DLBCL specific biomarkers, such asCITTA-BCL6, CLTC-ALK, IL21R-BCL6, PIM1-BCL6, TFCR-BCL6, IKZF1-BCL6 orSEC31A-ALK, or those listed in FIG. 28.

One or more DLBCL specific biomarkers, such as CITTA-BCL6, CLTC-ALK,IL21R-BCL6, PIM1-BCL6, TFCR-BCL6, IKZF1-BCL6 or SEC31A-ALK, or thoselisted in FIG. 28, can also be detected by one or more systems disclosedherein, for characterizing a DLBCL. For example, a detection system cancomprise one or more probes to detect one or more DLBCL specificbiomarkers, such as CITTA-BCL6, CLTC-ALK, IL21R-BCL6, PIM1-BCL6,TFCR-BCL6, IKZF1-BCL6 or SEC31A-ALK, or those listed in FIG. 28, of oneor more vesicles of a biological sample.

Burkitt's Lymphoma

Burkitt's lymphoma specific biomarkers from a vesicle can include one ormore (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 29,and can be used to create a Burkitt's lymphoma specific biosignature.For example, the biosignature can also comprise one or moreunderexpressed miRs such as, but not limited to, pri-miR-155, or anycombination thereof. The one or more mRNAs that may be analyzed caninclude, but are not limited to, MYC, TERT, NS, NP, MAZ, RCF3, BYSL,IDE3, CDC7, TCL1A, AUTS2, MYBL1, BMP7, ITPR3, CDC2, BACK2, TTK, MME,ALOX5, or TOP1, or any combination thereof and can be used as specificbiomarkers from a vesicle for Burkitt's lymphoma. The protein, ligand,or peptide that can be assessed in a vesicle can include, but is notlimited to, BCL6, KI-67, or any combination thereof.

The invention also provides an isolated vesicle comprising one or moreBurkitt's lymphoma specific biomarkers, such as IGH-MYC, LCP1-BCL6, orthose listed in FIG. 29. A composition comprising the isolated vesicleis also provided. Accordingly, in some embodiments, the compositioncomprises a population of vesicles comprising one or more Burkitt'slymphoma specific biomarkers, such as IGH-MYC, LCP1-BCL6, or thoselisted in FIG. 29. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for Burkitt's lymphoma specific vesicles orvesicles comprising one or more Burkitt's lymphoma specific biomarkers,such as IGH-MYC, LCP1-BCL6, or those listed in FIG. 29.

One or more Burkitt's lymphoma specific biomarkers, such as IGH-MYC,LCP1-BCL6, or those listed in FIG. 29, can also be detected by one ormore systems disclosed herein, for characterizing a Burkitt's lymphoma.For example, a detection system can comprise one or more probes todetect one or more Burkitt's lymphoma specific biomarkers, such asIGH-MYC, LCP1-BCL6, or those listed in FIG. 29, of one or more vesiclesof a biological sample.

Hepatocellular Carcinoma

Hepatocellular carcinoma specific biomarkers from a vesicle can includeone or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressedmiRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 30and can be used to create a hepatocellular carcinoma specificbiosignature. For example, the biosignature can comprise one or moreoverexpressed miRs, such as, but not limited to, miR-221. Thebiosignature can also comprise one or more underexpressed miRs such as,but not limited to, let-7a-1, let-7a-2, let-7a-3, let-7b, let-7c,let-7d, let-7e, let-7f-2, let-fg, miR-122a, miR-124a-2, miR-130a,miR-132, miR-136, miR-141, miR-142, miR-143, miR-145, miR-146, miR-150,miR-155(BIC), miR-181a-1, miR-181a-2, miR-181c, miR-195, miR-199a-1-5p,miR-199a-2-5p, miR-199b, miR-200b, miR-214, miR-223, or pre-miR-594, orany combination thereof. The one or more mRNAs that may be analyzed caninclude, but are not limited to, FAT10.

The one or more biomarkers of a biosignature can also be used tocharacterize hepatitis C virus-associated hepatocellular carcinoma. Theone or more biomarkers can be a miRNA, such as an overexpressed orunderexpressed miRNA. For example, the upregulated or overexpressedmiRNA can be miR-122, miR-100, or miR-10a and the downregulated miRNAcan be miR-198 or miR-145.

The invention also provides an isolated vesicle comprising one or morehepatocellular carcinoma specific biomarkers, such as listed in FIG. 30and in FIG. 1 for hepatocellular carcinoma. A composition comprising theisolated vesicle is also provided. Accordingly, in some embodiments, thecomposition comprises a population of vesicles comprising one or morehepatocellular carcinoma specific biomarkers, such as listed in FIG. 30and in FIG. 1 for hepatocellular carcinoma. The composition can comprisea substantially enriched population of vesicles, wherein the populationof vesicles is substantially homogeneous for hepatocellular carcinomaspecific vesicles or vesicles comprising one or more hepatocellularcarcinoma specific biomarkers, such as listed in FIG. 30 and in FIG. 1for hepatocellular carcinoma.

One or more hepatocellular carcinoma specific biomarkers, such as listedin FIG. 30 and in FIG. 1 for hepatocellular carcinoma, can also bedetected by one or more systems disclosed herein, for characterizing ahepatocellular carcinoma. For example, a detection system can compriseone or more probes to detect one or more hepatocellular carcinomaspecific biomarkers, such as listed in FIG. 30 and in FIG. 1 forhepatocellular carcinoma, of one or more vesicles of a biologicalsample.

Cervical Cancer

Cervical cancer specific biomarkers from a vesicle can include one ormore (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 31,and can be used to create a cervical cancer specific biosignature. Forexample, the one or more mRNAs that may be analyzed can include, but arenot limited to, HPV E6, HPV E7, or p53, or any combination thereof andcan be used as specific biomarkers from a vesicle for cervical cancer.

The invention also provides an isolated vesicle comprising one or morecervical cancer specific biomarkers, such as listed in FIG. 31 and inFIG. 1 for cervical cancer. A composition comprising the isolatedvesicle is also provided. Accordingly, in some embodiments, thecomposition comprises a population of vesicles comprising one or morecervical cancer specific biomarkers, such as listed in FIG. 31 and inFIG. 1 for cervical cancer. The composition can comprise a substantiallyenriched population of vesicles, wherein the population of vesicles issubstantially homogeneous for cervical cancer specific vesicles orvesicles comprising one or more cervical cancer specific biomarkers,such as listed in FIG. 31 and in FIG. 1 for cervical cancer.

One or more cervical cancer specific biomarkers, such as listed in FIG.31 and in FIG. 1 for cervical cancer, can also be detected by one ormore systems disclosed herein, for characterizing a cervical cancer. Forexample, a detection system can comprise one or more probes to detectone or more cervical cancer specific biomarkers, such as listed in FIG.31 and in FIG. 1 for cervical cancer, of one or more vesicles of abiological sample.

Endometrial Cancer

Endometrial cancer specific biomarkers from a vesicle can include one ormore (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 32and can be used to create a endometrial cancer specific biosignature.For example, the biosignature can comprise one or more overexpressedmiRs, such as, but not limited to, miR-185, miR-106a, miR-181a, miR-210,miR-423, miR-103, miR-107, or let-7c, or any combination thereof. Thebiosignature can also comprise one or more underexpressed miRs such as,but not limited to, miR-7i, miR-221, miR-193, miR-152, or miR-30c, orany combination thereof.

A biomarker mutation for endometrial cancer that can be assessed in avesicle includes, but is not limited to, a mutation of PTEN, K-RAS,B-catenin, p53, Her2/neu, or any combination of mutations specific forendometrial cancer. The protein, ligand, or peptide that can be assessedin a vesicle can include, but is not limited to, NLRP7, AlphaV Beta6integrin, or any combination thereof.

The invention also provides an isolated vesicle comprising one or moreendometrial cancer specific biomarkers, such as listed in FIG. 32 and inFIG. 1 for endometrial cancer. A composition comprising the isolatedvesicle is also provided. Accordingly, in some embodiments, thecomposition comprises a population of vesicles comprising one or moreendometrial cancer specific biomarkers, such as listed in FIG. 32 and inFIG. 1 for endometrial cancer. The composition can comprise asubstantially enriched population of vesicles, wherein the population ofvesicles is substantially homogeneous for endometrial cancer specificvesicles or vesicles comprising one or more endometrial cancer specificbiomarkers, such as listed in FIG. 32 and in FIG. 1 for endometrialcancer.

One or more endometrial cancer specific biomarkers, such as listed inFIG. 32 and in FIG. 1 for endometrial cancer, can also be detected byone or more systems disclosed herein, for characterizing a endometrialcancer. For example, a detection system can comprise one or more probesto detect one or more endometrial cancer specific biomarkers, such aslisted in FIG. 32 and in FIG. 1 for endometrial cancer, of one or morevesicles of a biological sample.

Head and Neck Cancer

Head and neck cancer specific biomarkers from a vesicle can include oneor more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 33,and can be used to create a head and neck cancer specific biosignature.For example, the biosignature can comprise one or more overexpressedmiRs, such as, but not limited to, miR-21, let-7, miR-18, miR-29c,miR-142-3p, miR-155, miR-146b, miR-205, or miR-21, or any combinationthereof. The biosignature can also comprise one or more underexpressedmiRs such as, but not limited to, miR-494. The one or more mRNAs thatmay be analyzed include, but are not limited to, HPV E6, HPV E7, p53,IL-8, SAT, H3FA3, or EGFR, or any combination thereof and can be used asspecific biomarkers from a vesicle for head and neck cancer.

A biomarker mutation for head and neck cancer that can be assessed in avesicle includes, but is not limited to, a mutation of GSTM1, GSTT1,GSTP1, OGG1, XRCC1, XPD, RAD51, EGFR, p53, or any combination ofmutations specific for head and neck cancer. The protein, ligand, orpeptide that can be assessed in a vesicle can include, but is notlimited to, EGFR, EphB4, or EphB2, or any combination thereof.

The invention also provides an isolated vesicle comprising one or morehead and neck cancer specific biomarkers, such as CHCHD7-PLAG1,CTNNB1-PLAG1, FHIT-HMGA2, HMGA2-NFIB, LIFR-PLAG1, or TCEA1-PLAG1, orthose listed in FIG. 33 and in FIG. 1 for head and neck cancer. Acomposition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more head and neck cancer specificbiomarkers, such as CHCHD7-PLAG1, CTNNB1-PLAG1, FHIT-HMGA2, HMGA2-NFIB,LIFR-PLAG1, or TCEA1-PLAG1, or those listed in FIG. 33 and in FIG. 1 forhead and neck cancer. The composition can comprise a substantiallyenriched population of vesicles, wherein the population of vesicles issubstantially homogeneous for head and neck cancer specific vesicles orvesicles comprising one or more head and neck cancer specificbiomarkers, such as CHCHD7-PLAG1, CTNNB1-PLAG1, FHIT-HMGA2, HMGA2-NFIB,LIFR-PLAG1, or TCEA1-PLAG1, or those listed in FIG. 33 and in FIG. 1 forhead and neck cancer.

One or more head and neck cancer specific biomarkers, such as listed inFIG. 33 and in FIG. 1 for head and neck cancer, can also be detected byone or more systems disclosed herein, for characterizing a head and neckcancer. For example, a detection system can comprise one or more probesto detect one or more head and neck cancer specific biomarkers, such asCHCHD7-PLAG1, CTNNB1-PLAG1, FHIT-HMGA2, HMGA2-NFIB, LIFR-PLAG1, orTCEA1-PLAG1, or those listed in FIG. 33 and in FIG. 1 for head and neckcancer, of one or more vesicles of a biological sample.

Inflammatory Bowel Disease (IBD)

IBD specific biomarkers from a vesicle can include one or more (forexample, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 34,and can be used to create a IBD specific biosignature. The one or moremRNAs that may be analyzed can include, but are not limited to,Trypsinogen IV, SERT, or any combination thereof and can be used asspecific biomarkers from a vesicle for IBD.

A biomarker mutation for IBD that can be assessed in a vesicle caninclude, but is not limited to, a mutation of CARD15 or any combinationof mutations specific for IBD. The protein, ligand, or peptide that canbe assessed in a vesicle can include, but is not limited to, II-16,II-1beta, II-12, TNF-alpha, interferon gamma, II-6, Rantes, MCP-1,Resistin, or 5-HT, or any combination thereof.

The invention also provides an isolated vesicle comprising one or moreIBD specific biomarkers, such as listed in FIG. 34 and in FIG. 1 forIBD. A composition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more IBD specific biomarkers, such aslisted in FIG. 34 and in FIG. 1 for IBD. The composition can comprise asubstantially enriched population of vesicles, wherein the population ofvesicles is substantially homogeneous for IBD specific vesicles orvesicles comprising one or more IBD specific biomarkers, such as listedin FIG. 34 and in FIG. 1 for IBD.

One or more IBD specific biomarkers, such as listed in FIG. 34 and inFIG. 1 for IBD, can also be detected by one or more systems disclosedherein, for characterizing a IBD. For example, a detection system cancomprise one or more probes to detect one or more IBD specificbiomarkers, such as listed in FIG. 34 and in FIG. 1 for IBD, of one ormore vesicles of a biological sample.

Diabetes

Diabetes specific biomarkers from a vesicle can include one or more (forexample, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 35,and can be used to create a diabetes specific biosignature. For example,the one or more mRNAs that may be analyzed can include, but are notlimited to, Il-8, CTSS, ITGB2, HLA-DRA, CD53, PLAG27, or MMP9, or anycombination thereof and can be used as specific biomarkers from avesicle for diabetes. The protein, ligand, or peptide that can beassessed in a vesicle can include, but is not limited to, RBP4.

The invention also provides an isolated vesicle comprising one or morediabetes specific biomarkers, such as listed in FIG. 35 and in FIG. 1for diabetes. A composition comprising the isolated vesicle is alsoprovided. Accordingly, in some embodiments, the composition comprises apopulation of vesicles comprising one or more diabetes specificbiomarkers, such as listed in FIG. 35 and in FIG. 1 for diabetes. Thecomposition can comprise a substantially enriched population ofvesicles, wherein the population of vesicles is substantiallyhomogeneous for diabetes specific vesicles or vesicles comprising one ormore diabetes specific biomarkers, such as listed in FIG. 35 and in FIG.1 for diabetes.

One or more diabetes specific biomarkers, such as listed in FIG. 35 andin FIG. 1 for diabetes, can also be detected by one or more systemsdisclosed herein, for characterizing diabetes. For example, a detectionsystem can comprise one or more probes to detect one or more diabetesspecific biomarkers, such as listed in FIG. 35 and in FIG. 1 fordiabetes, of one or more vesicles of a biological sample.

Barrett's Esophagus

Barrett's Esophagus specific biomarkers from a vesicle can include oneor more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 36,and can be used to create a Barrett's Esophagus specific biosignature.For example, the biosignature can comprise one or more overexpressedmiRs, such as, but not limited to, miR-21, miR-143, miR-145, miR-194, ormiR-215, or any combination thereof. The one or more mRNAs that may beanalyzed include, but are not limited to, S100A2, S100A4, or anycombination thereof and can be used as specific biomarkers from avesicle for Barrett's Esophagus.

A biomarker mutation for Barrett's Esophagus that can be assessed in avesicle includes, but is not limited to, a mutation of p53 or anycombination of mutations specific for Barrett's Esophagus. The protein,ligand, or peptide that can be assessed in a vesicle can include, but isnot limited to, p53, MUC1, MUC2, or any combination thereof.

The invention also provides an isolated vesicle comprising one or moreBarrett's Esophagus specific biomarkers, such as listed in FIG. 36 andin FIG. 1 for Barrett's Esophagus. A composition comprising the isolatedvesicle is also provided. Accordingly, in some embodiments, thecomposition comprises a population of vesicles comprising one or moreBarrett's Esophagus specific biomarkers, such as listed in FIG. 36 andin FIG. 1 for Barrett's Esophagus. The composition can comprise asubstantially enriched population of vesicles,

wherein the population of vesicles is substantially homogeneous forBarrett's Esophagus specific vesicles or vesicles comprising one or moreBarrett's Esophagus specific biomarkers, such as listed in FIG. 36 andin FIG. 1 for Barrett's Esophagus.

One or more Barrett's Esophagus specific biomarkers, such as listed inFIG. 36 and in FIG. 1 for Barrett's Esophagus, can also be detected byone or more systems disclosed herein, for characterizing a Barrett'sEsophagus. For example, a detection system can comprise one or moreprobes to detect one or more Barrett's Esophagus specific biomarkers,such as listed in FIG. 36 and in FIG. 1 for Barrett's Esophagus, of oneor more vesicles of a biological sample.

Fibromyalgia

Fibromyalgia specific biomarkers from a vesicle can include one or more(for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 37,and can be used to create a fibromyalgia specific biosignature. The oneor more mRNAs that may be analyzed can include, but are not limited to,NR2D which can be used as a specific biomarker from a vesicle forfibromyalgia.

The invention also provides an isolated vesicle comprising one or morefibromyalgia specific biomarkers, such as listed in FIG. 37 and in FIG.1 for fibromyalgia. A composition comprising the isolated vesicle isalso provided. Accordingly, in some embodiments, the compositioncomprises a population of vesicles comprising one or more fibromyalgiaspecific biomarkers, such as listed in FIG. 37 and in FIG. 1 forfibromyalgia. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for fibromyalgia specific vesicles or vesiclescomprising one or more fibromyalgia specific biomarkers, such as listedin FIG. 37 and in FIG. 1 for fibromyalgia.

One or more fibromyalgia specific biomarkers, such as listed in FIG. 37and in FIG. 1 for fibromyalgia, can also be detected by one or moresystems disclosed herein, for characterizing a fibromyalgia. Forexample, a detection system can comprise one or more probes to detectone or more fibromyalgia specific biomarkers, such as listed in FIG. 37and in FIG. 1 for fibromyalgia, of one or more vesicles of a biologicalsample.

Stroke

Stroke specific biomarkers from a vesicle can include one or more (forexample, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 38,and can be used to create a stroke specific biosignature. For example,the one or more mRNAs that may be analyzed can include, but are notlimited to, MMP9, S100-P, S100A12, S100A9, coag factor V, ArginaseI,CA-IV, monocarboxylic acid transporter, ets-2, EIF2alpha, cytoskeletonassociated protein 4, N-formylpeptide receptor, Ribonuclease2,N-acetylneuraminate pyruvate lyase, BCL-6, or Glycogen phosphorylase, orany combination thereof and can be used as specific biomarkers from avesicle for stroke.

The invention also provides an isolated vesicle comprising one or morestroke specific biomarkers, such as listed in FIG. 38 and in FIG. 1 forstroke. A composition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more stroke specific biomarkers, such aslisted in FIG. 38 and in FIG. 1 for stroke. The composition can comprisea substantially enriched population of vesicles, wherein the populationof vesicles is substantially homogeneous for stroke specific vesicles orvesicles comprising one or more stroke specific biomarkers, such aslisted in FIG. 38 and in FIG. 1 for stroke.

One or more stroke specific biomarkers, such as listed in FIG. 38 and inFIG. 1 for stroke, can also be detected by one or more systems disclosedherein, for characterizing a stroke. For example, a detection system cancomprise one or more probes to detect one or more stroke specificbiomarkers, such as listed in FIG. 38 and in FIG. 1 for stroke, of oneor more vesicles of a biological sample.

Multiple Sclerosis (MS)

MS specific biomarkers from a vesicle can include one or more (forexample, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 39,and can be used to create a MS specific biosignature. For example, theone or more mRNAs that may be analyzed can include, but are not limitedto, IL-6, IL-17, PAR-3, IL-17, T1/ST2, JunD, 5-LO, LTA4H, MBP, PLP, oralpha-beta crystallin, or any combination thereof and can be used asspecific biomarkers from a vesicle for MS.

The invention also provides an isolated vesicle comprising one or moreMS specific biomarkers, such as listed in FIG. 39 and in FIG. 1 for MS.A composition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more MS specific biomarkers, such aslisted in FIG. 39 and in FIG. 1 for MS. The composition can comprise asubstantially enriched population of vesicles, wherein the population ofvesicles is substantially homogeneous for MS specific vesicles orvesicles comprising one or more MS specific biomarkers, such as listedin FIG. 39 and in FIG. 1 for MS.

One or more MS specific biomarkers, such as listed in FIG. 39 and inFIG. 1 for MS, can also be detected by one or more systems disclosedherein, for characterizing a MS. For example, a detection system cancomprise one or more probes to detect one or more MS specificbiomarkers, such as listed in FIG. 39 and in FIG. 1 for MS, of one ormore vesicles of a biological sample.

Parkinson's Disease

Parkinson's disease specific biomarkers from a vesicle can include oneor more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 40,and can be used to create a Parkinson's disease specific biosignature.For example, the biosignature can include, but is not limited to, one ormore underexpressed miRs such as miR-133b. The one or more mRNAs thatmay be analyzed can include, but are not limited to Nurr1, BDNF, TrkB,gstm1, or 5100 beta, or any combination thereof and can be used asspecific biomarkers from a vesicle for Parkinson's disease.

A biomarker mutation for Parkinson's disease that can be assessed in avesicle includes, but is not limited to, a mutation of FGF20,alpha-synuclein, FGF20, NDUFV2, FGF2, CALB1, B2M, or any combination ofmutations specific for Parkinson's disease. The protein, ligand, orpeptide that can be assessed in a vesicle can include, but is notlimited to, apo-H, Ceruloplasmin, BDNF, IL-8, Beta2-microglobulin,apoAII, tau, ABeta1-42, DJ-1, or any combination thereof.

The invention also provides an isolated vesicle comprising one or moreParkinson's disease specific biomarkers, such as listed in FIG. 40 andin FIG. 1 for Parkinson's disease A composition comprising the isolatedvesicle is also provided. Accordingly, in some embodiments, thecomposition comprises a population of vesicles comprising one or moreParkinson's disease specific biomarkers, such as listed in FIG. 40 andin FIG. 1 for Parkinson's disease. The composition can comprise asubstantially enriched population of vesicles, wherein the population ofvesicles is substantially homogeneous for Parkinson's disease specificvesicles or vesicles comprising one or more Parkinson's disease specificbiomarkers, such as listed in FIG. 40 and in FIG. 1 for Parkinson'sdisease.

One or more Parkinson's disease specific biomarkers, such as listed inFIG. 40 and in FIG. 1 for Parkinson's disease, can also be detected byone or more systems disclosed herein, for characterizing a Parkinson'sdisease. For example, a detection system can comprise one or more probesto detect one or more Parkinson's disease specific biomarkers, such aslisted in FIG. 40 and in FIG. 1 for Parkinson's disease, of one or morevesicles of a biological sample.

Rheumatic Disease

Rheumatic disease specific biomarkers from a vesicle can include one ormore (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 41,and can be used to create a rheumatic disease specific biosignature. Forexample, the biosignature can also comprise one or more underexpressedmiRs such as, but not limited to, miR-146a, miR-155, miR-132, miR-16, ormiR-181, or any combination thereof. The one or more mRNAs that may beanalyzed can include, but are not limited to, HOXD10, HOXD11, HOXD13,CCL8, LIM homeobox2, or CENP-E, or any combination thereof and can beused as specific biomarkers from a vesicle for rheumatic disease. Theprotein, ligand, or peptide that can be assessed in a vesicle caninclude, but is not limited to, TNFα.

The invention also provides an isolated vesicle comprising one or morerheumatic disease specific biomarkers, such as listed in FIG. 41 and inFIG. 1 for rheumatic disease. A composition comprising the isolatedvesicle is also provided. Accordingly, in some embodiments, thecomposition comprises a population of vesicles comprising one or morerheumatic disease specific biomarkers, such as listed in FIG. 41 and inFIG. 1 for rheumatic disease. The composition can comprise asubstantially enriched population of vesicles, wherein the population ofvesicles is substantially homogeneous for rheumatic disease specificvesicles or vesicles comprising one or more rheumatic disease specificbiomarkers, such as listed in FIG. 41 and in FIG. 1 for rheumaticdisease.

One or more rheumatic disease specific biomarkers, such as listed inFIG. 41 and in FIG. 1 for rheumatic disease, can also be detected by oneor more systems disclosed herein, for characterizing a rheumaticdisease. For example, a detection system can comprise one or more probesto detect one or more rheumatic disease specific biomarkers, such aslisted in FIG. 41 and in FIG. 1 for rheumatic disease, of one or morevesicles of a biological sample.

Alzheimer's Disease

Alzheimer's disease specific biomarkers from a vesicle can include oneor more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 42,and can be used to create a Alzheimers disease specific biosignature.For example, the biosignature can also comprise one or moreunderexpressed miRs such as miR-107, miR-29a, miR-29b-1, or miR-9, orany combination thereof. The biosignature can also comprise one or moreoverexpressed miRs such as miR-128 or any combination thereof.

The one or more mRNAs that may be analyzed can include, but are notlimited to, HIF-1α, BACE1, Reelin, CHRNA7, or 3Rtau/4Rtau, or anycombination thereof and can be used as specific biomarkers from avesicle for Alzheimer's disease.

A biomarker mutation for Alzheimer's disease that can be assessed in avesicle includes, but is not limited to, a mutation of APP, presenilin1,presenilin2, APOE4, or any combination of mutations specific forAlzheimer's disease. The protein, ligand, or peptide that can beassessed in a vesicle can include, but is not limited to, BACE1, Reelin,Cystatin C, Truncated Cystatin C, Amyloid Beta, C3a, t-Tau, Complementfactor H, or alpha-2-macroglobulin, or any combination thereof.

The invention also provides an isolated vesicle comprising one or moreAlzheimer's disease specific biomarkers, such as listed in FIG. 42 andin FIG. 1 for Alzheimer's disease. A composition comprising the isolatedvesicle is also provided. Accordingly, in some embodiments, thecomposition comprises a population of vesicles comprising one or moreAlzheimer's disease specific biomarkers, such as listed in FIG. 42 andin FIG. 1 for Alzheimer's disease. The composition can comprise asubstantially enriched population of vesicles, wherein the population ofvesicles is substantially homogeneous for Alzheimer's disease specificvesicles or vesicles comprising one or more Alzheimer's disease specificbiomarkers, such as listed in FIG. 42 and in FIG. 1 for Alzheimer'sdisease.

One or more Alzheimer's disease specific biomarkers, such as listed inFIG. 42 and in FIG. 1 for Alzheimer's disease, can also be detected byone or more systems disclosed herein, for characterizing a Alzheimer'sdisease. For example, a detection system can comprise one or more probesto detect one or more Alzheimer's disease specific biomarkers, such aslisted in FIG. 42 and in FIG. 1 for Alzheimer's disease, of one or morevesicles of a biological sample.

Prion Disease

Prion specific biomarkers from a vesicle can include one or more (forexample, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 43,and can be used to create a prion specific biosignature. For example,the one or more mRNAs that may be analyzed can include, but are notlimited to, Amyloid B4, App, IL-1R1, or SOD1, or any combination thereofand can be used as specific biomarkers from a vesicle for a prion. Theprotein, ligand, or peptide that can be assessed in a vesicle caninclude, but is not limited to, PrP(c), 14-3-3, NSE, S-100, Tau, AQP-4,or any combination thereof.

The invention also provides an isolated vesicle comprising one or moreprion disease specific biomarkers, such as listed in FIG. 43 and in FIG.1 for prion disease. A composition comprising the isolated vesicle isalso provided. Accordingly, in some embodiments, the compositioncomprises a population of vesicles comprising one or more prion diseasespecific biomarkers, such as listed in FIG. 43 and in FIG. 1 for priondisease. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for prion disease specific vesicles orvesicles comprising one or more prion disease specific biomarkers, suchas listed in FIG. 43 and in FIG. 1 for prion disease.

One or more prion disease specific biomarkers, such as listed in FIG. 43and in FIG. 1 for prion disease, can also be detected by one or moresystems disclosed herein, for characterizing a prion disease. Forexample, a detection system can comprise one or more probes to detectone or more prion disease specific biomarkers, such as listed in FIG. 43and in FIG. 1 for prion disease, of one or more vesicles of a biologicalsample.

Sepsis

Sepsis specific biomarkers from a vesicle can include one or more (forexample, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 44,and can be used to create a sepsis specific biosignature. For example,the one or more mRNAs that may be analyzed can include, but are notlimited to, 15-Hydroxy-PG dehydrogenase (up), LAIR1 (up), NFKB1A (up),TLR2, PGLYPR1, TLR4, MD2, TLR5, IFNAR2, IRAK2, IRAK3, IRAK4, PI3K,PI3KCB, MAP2K6, MAPK14, NFKB1A, NFKB1, IL1R1, MAP2K1IP1, MKNK1, FAS,CASP4, GADD45B, SOCS3, TNFSF10, TNFSF13B, OSM, HGF, or IL18R1, or anycombination thereof and can be used as specific biomarkers from avesicle for sepsis.

The invention also provides an isolated vesicle comprising one or moresepsis specific biomarkers, such as listed in FIG. 44. A compositioncomprising the isolated vesicle is also provided. Accordingly, in someembodiments, the composition comprises a population of vesiclescomprising one or more sepsis specific biomarkers, such as listed inFIG. 44. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for sepsis specific vesicles or vesiclescomprising one or more sepsis specific biomarkers, such as listed inFIG. 44.

One or more sepsis specific biomarkers, such as listed in FIG. 44, canalso be detected by one or more systems disclosed herein, forcharacterizing a sepsis. For example, a detection system can compriseone or more probes to detect one or more sepsis specific biomarkers,such as listed in FIG. 44, of one or more vesicles of a biologicalsample.

Chronic Neuropathic Pain

Chronic neuropathic pain (CNP) specific biomarkers from a vesicle caninclude one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more)overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations,proteins, ligands, peptides, snoRNA, or any combination thereof, such aslisted in FIG. 45, and can be used to create a CNP specificbiosignature. For example, the one or more mRNAs that may be analyzedcan include, but are not limited to, ICAM-1 (rodent), CGRP (rodent),TIMP-1 (rodent), CLR-1 (rodent), HSP-27 (rodent), FABP (rodent), orapolipoprotein D (rodent), or any combination thereof and can be used asspecific biomarkers from a vesicle for CNP. The protein, ligand, orpeptide that can be assessed in a vesicle can include, but is notlimited to, chemokines, chemokine receptors (CCR2/4), or any combinationthereof.

The invention also provides an isolated vesicle comprising one or morechronic neuropathic pain specific biomarkers, such as listed in FIG. 45and in FIG. 1 for chronic neuropathic pain. A composition comprising theisolated vesicle is also provided. Accordingly, in some embodiments, thecomposition comprises a population of vesicles comprising one or morechronic neuropathic pain specific biomarkers, such as listed in FIG. 45and in FIG. 1 for chronic neuropathic pain. The composition can comprisea substantially enriched population of vesicles, wherein the populationof vesicles is substantially homogeneous for chronic neuropathic painspecific vesicles or vesicles comprising one or more chronic neuropathicpain specific biomarkers, such as listed in FIG. 45 and in FIG. 1 forchronic neuropathic pain.

One or more chronic neuropathic pain specific biomarkers, such as listedin FIG. 45 and in FIG. 1 for chronic neuropathic pain, can also bedetected by one or more systems disclosed herein, for characterizing achronic neuropathic pain. For example, a detection system can compriseone or more probes to detect one or more chronic neuropathic painspecific biomarkers, such as listed in FIG. 45 and in FIG. 1 for chronicneuropathic pain, of one or more vesicles of a biological sample.

Peripheral Neuropathic Pain

Peripheral neuropathic pain (PNP) specific biomarkers from a vesicle caninclude one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more)overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations,proteins, ligands, peptides, snoRNA, or any combination thereof, such aslisted in FIG. 46, and can be used to create a PNP specificbiosignature. For example, the protein, ligand, or peptide that can beassessed in a vesicle can include, but is not limited to, OX42, ED9, orany combination thereof.

The invention also provides an isolated vesicle comprising one or moreperipheral neuropathic pain specific biomarkers, such as listed in FIG.46 and in FIG. 1 for peripheral neuropathic pain. A compositioncomprising the isolated vesicle is also provided. Accordingly, in someembodiments, the composition comprises a population of vesiclescomprising one or more peripheral neuropathic pain specific biomarkers,such as listed in FIG. 46 and in FIG. 1 for peripheral neuropathic pain.The composition can comprise a substantially enriched population ofvesicles, wherein the population of vesicles is substantiallyhomogeneous for peripheral neuropathic pain specific vesicles orvesicles comprising one or more peripheral neuropathic pain specificbiomarkers, such as listed in FIG. 46 and in FIG. 1 for peripheralneuropathic pain.

One or more peripheral neuropathic pain specific biomarkers, such aslisted in FIG. 46 and in FIG. 1 for peripheral neuropathic pain, canalso be detected by one or more systems disclosed herein, forcharacterizing a peripheral neuropathic pain. For example, a detectionsystem can comprise one or more probes to detect one or more peripheralneuropathic pain specific biomarkers, such as listed in FIG. 46 and inFIG. 1 for peripheral neuropathic pain, of one or more vesicles of abiological sample.

Schizophrenia

Schizophrenia specific biomarkers from a vesicle can include one or more(for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 47,and can be used to create a schizophrenia specific biosignature. Forexample, the biosignature can comprise one or more overexpressed miRs,such as, but not limited to, miR-181b. The biosignature can alsocomprise one or more underexpressed miRs such as, but not limited to,miR-7, miR-24, miR-26b, miR-29b, miR-30b, miR-30e, miR-92, or miR-195,or any combination thereof.

The one or more mRNAs that may be analyzed can include, but are notlimited to, IFITM3, SERPINA3, GLS, or ALDH7A1BASP1, or any combinationthereof and can be used as specific biomarkers from a vesicle forschizophrenia. A biomarker mutation for schizophrenia that can beassessed in a vesicle includes, but is not limited to, a mutation of toDISC1, dysbindin, neuregulin-1, seratonin 2a receptor, NURR1, or anycombination of mutations specific for schizophrenia.

The protein, ligand, or peptide that can be assessed in a vesicle caninclude, but is not limited to, ATP5B, ATP5H, ATP6V1B, DNM1, NDUFV2,NSF, PDHB, or any combination thereof.

The invention also provides an isolated vesicle comprising one or moreschizophrenia specific biomarkers, such as listed in FIG. 47 and in FIG.1 for schizophrenia. A composition comprising the isolated vesicle isalso provided. Accordingly, in some embodiments, the compositioncomprises a population of vesicles comprising one or more schizophreniaspecific biomarkers, such as listed in FIG. 47 and in FIG. 1 forschizophrenia. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for schizophrenia specific vesicles orvesicles comprising one or more schizophrenia specific biomarkers, suchas listed in FIG. 47 and in FIG. 1 for schizophrenia.

One or more schizophrenia specific biomarkers, such as listed in FIG. 47and in FIG. 1 for schizophrenia, can also be detected by one or moresystems disclosed herein, for characterizing a schizophrenia. Forexample, a detection system can comprise one or more probes to detectone or more schizophrenia specific biomarkers, such as listed in FIG. 47and in FIG. 1 for schizophrenia, of one or more vesicles of a biologicalsample.

Bipolar Disease

Bipolar disease specific biomarkers from a vesicle can include one ormore (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 48,and can be used to create a bipolar disease specific biosignature. Forexample, the one or more mRNAs that may be analyzed can include, but arenot limited to, FGF2, ALDH7A1, AGXT2L1, AQP4, or PCNT2, or anycombination thereof and can be used as specific biomarkers from avesicle for bipolar disease. A biomarker mutation for bipolar diseasethat can be assessed in a vesicle includes, but is not limited to, amutation of Dysbindin, DAOA/G30, DISC1, neuregulin-1, or any combinationof mutations specific for bipolar disease.

The invention also provides an isolated vesicle comprising one or morebipolar disease specific biomarkers, such as listed in FIG. 48. Acomposition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more bipolar disease specific biomarkers,such as listed in FIG. 48. The composition can comprise a substantiallyenriched population of vesicles, wherein the population of vesicles issubstantially homogeneous for bipolar disease specific vesicles orvesicles comprising one or more bipolar disease specific biomarkers,such as listed in FIG. 48.

One or more bipolar disease specific biomarkers, such as listed in FIG.48, can also be detected by one or more systems disclosed herein, forcharacterizing a bipolar disease. For example, a detection system cancomprise one or more probes to detect one or more bipolar diseasespecific biomarkers, such as listed in FIG. 48, of one or more vesiclesof a biological sample.

Depression

Depression specific biomarkers from a vesicle can include one or more(for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 49,and can be used to create a depression specific biosignature. Forexample, the one or more mRNAs that may be analyzed can include, but arenot limited to, FGFR1, FGFR2, FGFR3, or AQP4, or any combination thereofcan also be used as specific biomarkers from a vesicle for depression.

The invention also provides an isolated vesicle comprising one or moredepression specific biomarkers, such as listed in FIG. 49. A compositioncomprising the isolated vesicle is also provided. Accordingly, in someembodiments, the composition comprises a population of vesiclescomprising one or more depression specific biomarkers, such as listed inFIG. 49. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for depression specific vesicles or vesiclescomprising one or more depression specific biomarkers, such as listed inFIG. 49.

One or more depression specific biomarkers, such as listed in FIG. 49,can also be detected by one or more systems disclosed herein, forcharacterizing a depression. For example, a detection system cancomprise one or more probes to detect one or more depression specificbiomarkers, such as listed in FIG. 49, of one or more vesicles of abiological sample.

Gastrointestinal Stromal Tumor (GIST)

GIST specific biomarkers from a vesicle can include one or more (forexample, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 50,and can be used to create a GIST specific biosignature. For example, theone or more mRNAs that may be analyzed can include, but are not limitedto, DOG-1, PKC-theta, KIT, GPR20, PRKCQ, KCNK3, KCNH2, SCG2, TNFRSF6B,or CD34, or any combination thereof and can be used as specificbiomarkers from a vesicle for GIST.

A biomarker mutation for GIST that can be assessed in a vesicleincludes, but is not limited to, a mutation of PKC-theta or anycombination of mutations specific for GIST. The protein, ligand, orpeptide that can be assessed in a vesicle can include, but is notlimited to, PDGFRA, c-kit, or any combination thereof.

The invention also provides an isolated vesicle comprising one or moreGIST specific biomarkers, such as listed in FIG. 50 and in FIG. 1 forGIST. A composition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more GIST specific biomarkers, such aslisted in FIG. 50 and in FIG. 1 for GIST. The composition can comprise asubstantially enriched population of vesicles, wherein the population ofvesicles is substantially homogeneous for GIST specific vesicles orvesicles comprising one or more GIST specific biomarkers, such as listedin FIG. 50 and in FIG. 1 for GIST.

One or more GIST specific biomarkers, such as listed in FIG. 50 and inFIG. 1 for GIST, can also be detected by one or more systems disclosedherein, for characterizing a GIST. For example, a detection system cancomprise one or more probes to detect one or more GIST specificbiomarkers, such as listed in FIG. 50 and in FIG. 1 for GIST, of one ormore vesicles of a biological sample.

Renal Cell Carcinoma

Renal cell carcinoma specific biomarkers from a vesicle can include oneor more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 51,and can be used to create a renal cell carcinoma specific biosignature.For example, the biosignature can also comprise one or moreunderexpressed miRs such as, but not limited to, miR-141, miR-200c, orany combination thereof. The one or more upregulated or overexpressedmiRNA can be miR-28, miR-185, miR-27, miR-let-7f-2, or any combinationthereof.

The one or more mRNAs that may be analyzed can include, but are notlimited to, laminin receptor 1, betaig-h3, Galectin-1, a-2Macroglobulin, Adipophilin, Angiopoietin 2, Caldesmon 1, Class IIMHC-associated invariant chain (CD74), Collagen IV-al, Complementcomponent, Complement component 3, Cytochrome P450, subfamily Illpolypeptide 2, Delta sleep-inducing peptide, Fc g receptor IIIa (CD16),HLA-B, HLA-DRa, HLA-DRb, HLA-SB, IFN-induced transmembrane protein 3,IFN-induced transmembrane protein 1, or Lysyl Oxidase, or anycombination thereof and can be used as specific biomarkers from avesicle for renal cell carcinoma.

A biomarker mutation for renal cell carcinoma that can be assessed in avesicle includes, but is not limited to, a mutation of VHL or anycombination of mutations specific renal cell carcinoma.

The protein, ligand, or peptide that can be assessed in a vesicle caninclude, but is not limited to, IF1alpha, VEGF, PDGFRA, or anycombination thereof.

The invention also provides an isolated vesicle comprising one or moreRCC specific biomarkers, such as ALPHA-TFEB, NONO-TFE3, PRCC-TFE3,SFPQ-TFE3, CLTC-TFE3, or MALAT1-TFEB, or those listed in FIG. 51 and inFIG. 1 for RCC. A composition comprising the isolated vesicle is alsoprovided. Accordingly, in some embodiments, the composition comprises apopulation of vesicles comprising one or more RCC specific biomarkers,such as ALPHA-TFEB, NONO-TFE3, PRCC-TFE3, SFPQ-TFE3, CLTC-TFE3, orMALAT1-TFE, or those listed in FIG. 51 and in FIG. 1 for RCC. Thecomposition can comprise a substantially enriched population ofvesicles, wherein the population of vesicles is substantiallyhomogeneous for RCC specific vesicles or vesicles comprising one or moreRCC specific biomarkers, such as ALPHA-TFEB, NONO-TFE3, PRCC-TFE3,SFPQ-TFE3, CLTC-TFE3, or MALAT1-TFE, or those listed in FIG. 51 and inFIG. 1 for RCC.

One or more RCC specific biomarkers, such as ALPHA-TFEB, NONO-TFE3,PRCC-TFE3, SFPQ-TFE3, CLTC-TFE3, or MALAT1-TFE, or those listed in FIG.51 and in FIG. 1 for RCC, can also be detected by one or more systemsdisclosed herein, for characterizing a RCC. For example, a detectionsystem can comprise one or more probes to detect one or more RCCspecific biomarkers, such as ALPHA-TFEB, NONO-TFE3, PRCC-TFE3,SFPQ-TFE3, CLTC-TFE3, or MALAT1-TFE, or those listed in FIG. 51 and inFIG. 1 for RCC, of one or more vesicles of a biological sample.

Cirrhosis

Cirrhosis specific biomarkers from a vesicle can include one or more(for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 52,and can be used to create a cirrhosis specific biosignature. The one ormore mRNAs that may be analyzed include, but are not limited to, NLT,which can be used as aspecific biomarker from a vesicle for cirrhosis.

The protein, ligand, or peptide that can be assessed in a vesicle caninclude, but is not limited to, NLT, HBsAG, AST, YKL-40, Hyaluronicacid, TIMP-1, alpha 2 macroglobulin, a-1-antitrypsin P1Z allele,haptoglobin, or acid phosphatase ACP AC, or any combination thereof.

The invention also provides an isolated vesicle comprising one or morecirrhosis specific biomarkers, such as those listed in FIG. 52 and inFIG. 1 for cirrhosis. A composition comprising the isolated vesicle isalso provided. Accordingly, in some embodiments, the compositioncomprises a population of vesicles comprising one or more cirrhosisspecific biomarkers, such as those listed in FIG. 52 and in FIG. 1 forcirrhosis. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for cirrhosis specific vesicles or vesiclescomprising one or more cirrhosis specific biomarkers, such as thoselisted in FIG. 52 and in FIG. 1 for cirrhosis.

One or more cirrhosis specific biomarkers, such as those listed in FIG.52 and in FIG. 1 for cirrhosis, can also be detected by one or moresystems disclosed herein, for characterizing cirrhosis. For example, adetection system can comprise one or more probes to detect one or morecirrhosis specific biomarkers, such as those listed in FIG. 52 and inFIG. 1 for cirrhosis, of one or more vesicles of a biological sample.

Esophageal Cancer

Esophageal cancer specific biomarkers from a vesicle can include one ormore (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 53,and can be used to create a esophageal cancer specific biosignature. Forexample, the biosignature can comprise one or more overexpressed miRs,such as, but not limited to, miR-192, miR-194, miR-21, miR-200c, miR-93,miR-342, miR-152, miR-93, miR-25, miR-424, or miR-151, or anycombination thereof. The biosignature can also comprise one or moreunderexpressed miRs such as, but not limited to, miR-27b, miR-205,miR-203, miR-342, let-7c, miR-125b, miR-100, miR-152, miR-192, miR-194,miR-27b, miR-205, miR-203, miR-200c, miR-99a, miR-29c, miR-140, miR-103,or miR-107, or any combination thereof. The one or more mRNAs that maybe analyzed include, but are not limited to, MTHFR and can be used asspecific biomarkers from a vesicle for esophageal cancer.

The invention also provides an isolated vesicle comprising one or moreesophageal cancer specific biomarkers, such as listed in FIG. 53 and inFIG. 1 for esophageal cancer. A composition comprising the isolatedvesicle is also provided. Accordingly, in some embodiments, thecomposition comprises a population of vesicles comprising one or moreesophageal cancer specific biomarkers, such as listed in FIG. 53 and inFIG. 1 for esophageal cancer. The composition can comprise asubstantially enriched population of vesicles, wherein the population ofvesicles is substantially homogeneous for esophageal cancer specificvesicles or vesicles comprising one or more esophageal cancer specificbiomarkers, such as listed in FIG. 53 and in FIG. 1 for esophagealcancer.

One or more esophageal cancer specific biomarkers, such as listed inFIG. 53 and in FIG. 1 for esophageal cancer, can also be detected by oneor more systems disclosed herein, for characterizing a esophagealcancer. For example, a detection system can comprise one or more probesto detect one or more esophageal cancer specific biomarkers, such aslisted in FIG. 53 and in FIG. 1 for esophageal cancer, of one or morevesicles of a biological sample.

Gastric Cancer

Gastric cancer specific biomarkers from a vesicle can include one ormore (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 54,and can be used to create a gastric cancer specific biosignature. Forexample, the biosignature can comprise one or more overexpressed miRs,such as, but not limited to, miR-106a, miR-21, miR-191, miR-223,miR-24-1, miR-24-2, miR-107, miR-92-2, miR-214, miR-25, or miR-221, orany combination thereof. The biosignature can also comprise one or moreunderexpressed miRs such as, but not limited to, let-7a.

The one or more mRNAs that may be analyzed include, but are not limitedto, RRM2, EphA4, or survivin, or any combination thereof and can be usedas specific biomarkers from a vesicle for gastric cancer. A biomarkermutation for gastric cancer that can be assessed in a vesicle includes,but is not limited to, a mutation of APC or any combination of mutationsspecific for gastric cancer. The protein, ligand, or peptide that can beassessed in a vesicle can include, but is not limited to EphA4.

The invention also provides an isolated vesicle comprising one or moregastric cancer specific biomarkers, such as listed in FIG. 54. Acomposition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more gastric cancer specific biomarkers,such as listed in FIG. 54. The composition can comprise a substantiallyenriched population of vesicles, wherein the population of vesicles issubstantially homogeneous for gastric cancer specific vesicles orvesicles comprising one or more gastric cancer specific biomarkers, suchas listed in FIG. 54.

One or more gastric cancer specific biomarkers, such as listed in FIG.54, can also be detected by one or more systems disclosed herein, forcharacterizing a gastric cancer. For example, a detection system cancomprise one or more probes to detect one or more gastric cancerspecific biomarkers, such as listed in FIG. 54, of one or more vesiclesof a biological sample.

Autism

Autism specific biomarkers from a vesicle can include one or more (forexample, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 55,and can be used to create an autism specific biosignature. For example,the biosignature can comprise one or more overexpressed miRs, such as,but not limited to, miR-484, miR-21, miR-212, miR-23a, miR-598, miR-95,miR-129, miR-431, miR-7, miR-15a, miR-27a, miR-15b, miR-148b, miR-132,or miR-128, or any combination thereof. The biosignature can alsocomprise one or more underexpressed miRs such as, but not limited to,miR-93, miR-106a, miR-539, miR-652, miR-550, miR-432, miR-193b,miR-181d, miR-146b, miR-140, miR-381, miR-320a, or miR-106b, or anycombination thereof. The protein, ligand, or peptide that can beassessed in a vesicle can include, but is not limited to, GM1, GD1a,GD1b, or GT1b, or any combination thereof.

The invention also provides an isolated vesicle comprising one or moreautism specific biomarkers, such as listed in FIG. 55 and in FIG. 1 forautism. A composition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more autism specific biomarkers, such aslisted in FIG. 55 and in FIG. 1 for autism. The composition can comprisea substantially enriched population of vesicles, wherein the populationof vesicles is substantially homogeneous for autism specific vesicles orvesicles comprising one or more autism specific biomarkers, such aslisted in FIG. 55 and in FIG. 1 for autism.

One or more autism specific biomarkers, such as listed in FIG. 55 and inFIG. 1 for autism, can also be detected by one or more systems disclosedherein, for characterizing a autism. For example, a detection system cancomprise one or more probes to detect one or more autism specificbiomarkers, such as listed in FIG. 55 and in FIG. 1 for autism, of oneor more vesicles of a biological sample.

Organ Rejection

Organ rejection specific biomarkers from a vesicle can include one ormore (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 56,and can be used to create an organ rejection specific biosignature. Forexample, the biosignature can comprise one or more overexpressed miRs,such as, but not limited to, miR-658, miR-125a, miR-320, miR-381,miR-628, miR-602, miR-629, or miR-125a, or any combination thereof. Thebiosignature can also comprise one or more underexpressed miRs such as,but not limited to, miR-324-3p, miR-611, miR-654, miR-330_MM1, miR-524,miR-17-3p_MM1, miR-483, miR-663, miR-516-5p, miR-326, miR-197_MM2, ormiR-346, or any combination thereof. The protein, ligand, or peptidethat can be assessed in a vesicle can include, but is not limited to,matix metalloprotein-9, proteinase 3, or HNP, or any combinationsthereof. The biomarker can be a member of the matrix metalloproteinases.

The invention also provides an isolated vesicle comprising one or moreorgan rejection specific biomarkers, such as listed in FIG. 56. Acomposition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more organ rejection specific biomarkers,such as listed in FIG. 56. The composition can comprise a substantiallyenriched population of vesicles, wherein the population of vesicles issubstantially homogeneous for organ rejection specific vesicles orvesicles comprising one or more organ rejection specific biomarkers,such as listed in FIG. 56.

One or more organ rejection specific biomarkers, such as listed in FIG.56, can also be detected by one or more systems disclosed herein, forcharacterizing a organ rejection. For example, a detection system cancomprise one or more probes to detect one or more organ rejectionspecific biomarkers, such as listed in FIG. 56, of one or more vesiclesof a biological sample.

Methicillin-Resistant Staphylococcus aureus

Methicillin-resistant Staphylococcus aureus specific biomarkers from avesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, ormore) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations,proteins, ligands, peptides, snoRNA, or any combination thereof, such aslisted in FIG. 57, and can be used to create a methicillin-resistantStaphylococcus aureus specific biosignature.

The one or more mRNAs that may be analyzed include, but are not limitedto, TSST-1 which can be used as a specific biomarker from a vesicle formethicillin-resistant Staphylococcus aureus. A biomarker mutation formethicillin-resistant Staphylococcus aureus that can be assessed in avesicle includes, but is not limited to, a mutation of mecA, Protein ASNPs, or any combination of mutations specific for methicillin-resistantStaphylococcus aureus. The protein, ligand, or peptide that can beassessed in a vesicle can include, but is not limited to, ETA, ETB,TSST-1, or leukocidins, or any combination thereof.

The invention also provides an isolated vesicle comprising one or moremethicillin-resistant Staphylococcus aureus specific biomarkers, such aslisted in FIG. 57. A composition comprising the isolated vesicle is alsoprovided. Accordingly, in some embodiments, the composition comprises apopulation of vesicles comprising one or more methicillin-resistantStaphylococcus aureus specific biomarkers, such as listed in FIG. 57.The composition can comprise a substantially enriched population ofvesicles, wherein the population of vesicles is substantiallyhomogeneous for methicillin-resistant Staphylococcus aureus specificvesicles or vesicles comprising one or more methicillin-resistantStaphylococcus aureus specific biomarkers, such as listed in FIG. 57.

One or more methicillin-resistant Staphylococcus aureus specificbiomarkers, such as listed in FIG. 57, can also be detected by one ormore systems disclosed herein, for characterizing amethicillin-resistant Staphylococcus aureus. For example, a detectionsystem can comprise one or more probes to detect one or moremethicillin-resistant Staphylococcus aureus specific biomarkers, such aslisted in FIG. 57, of one or more vesicles of a biological sample.

Vulnerable Plaque

Vulnerable plaque specific biomarkers from a vesicle can include one ormore (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs,underexpressed miRs, mRNAs, genetic mutations, proteins, ligands,peptides, snoRNA, or any combination thereof, such as listed in FIG. 58,and can be used to create a vulnerable plaque specific biosignature. Theprotein, ligand, or peptide that can be assessed in a vesicle caninclude, but is not limited to, IL-6, MMP-9, PAPP-A, D-dimer,fibrinogen, Lp-PLA2, SCD40L, Il-18, oxLDL, GPx-1, MCP-1, PIGF, or CRP,or any combination thereof.

The invention also provides an isolated vesicle comprising one or morevulnerable plaque specific biomarkers, such as listed in FIG. 58 and inFIG. 1 for vulnerable plaque. A composition comprising the isolatedvesicle is also provided. Accordingly, in some embodiments, thecomposition comprises a population of vesicles comprising one or morevulnerable plaque specific biomarkers, such as listed in FIG. 58 and inFIG. 1 for vulnerable plaque. The composition can comprise asubstantially enriched population of vesicles, wherein the population ofvesicles is substantially homogeneous for vulnerable plaque specificvesicles or vesicles comprising one or more vulnerable plaque specificbiomarkers, such as listed in FIG. 58 and in FIG. 1 for vulnerableplaque.

One or more vulnerable plaque specific biomarkers, such as listed inFIG. 58 and in FIG. 1 for vulnerable plaque, can also be detected by oneor more systems disclosed herein, for characterizing a vulnerableplaque. For example, a detection system can comprise one or more probesto detect one or more vulnerable plaque specific biomarkers, such aslisted in FIG. 58 and in FIG. 1 for vulnerable plaque, of one or morevesicles of a biological sample.

Autoimmune Disease

The invention also provides an isolated vesicle comprising one or moreautoimmune disease specific biomarkers, such as listed in FIG. 1 forautoimmune disease. A composition comprising the isolated vesicle isalso provided. Accordingly, in some embodiments, the compositioncomprises a population of vesicles comprising one or more autoimmunedisease specific biomarkers, such as listed in FIG. 1 for autoimmunedisease. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for autoimmune disease specific vesicles orvesicles comprising one or more autoimmune disease specific biomarkers,such as listed in FIG. 1 for autoimmune disease.

One or more autoimmune disease specific biomarkers, such as listed inFIG. 1 for autoimmune disease, can also be detected by one or moresystems disclosed herein, for characterizing a autoimmune disease. Forexample, a detection system can comprise one or more probes to detectone or more autoimmune disease specific biomarkers, such as listed inFIG. 1 for autoimmune disease, of one or more vesicles of a biologicalsample.

Tuberculosis (TB)

The invention also provides an isolated vesicle comprising one or moreTB disease specific biomarkers, such as listed in FIG. 1 for TB disease.A composition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more TB disease specific biomarkers, suchas listed in FIG. 1 for TB disease. The composition can comprise asubstantially enriched population of vesicles, wherein the population ofvesicles is substantially homogeneous for TB disease specific vesiclesor vesicles comprising one or more TB disease specific biomarkers, suchas listed in FIG. 1 for TB disease.

One or more TB disease specific biomarkers, such as listed in FIG. 1 forTB disease, can also be detected by one or more systems disclosedherein, for characterizing a TB disease. For example, a detection systemcan comprise one or more probes to detect one or more TB diseasespecific biomarkers, such as listed in FIG. 1 for TB disease, of one ormore vesicles of a biological sample.

HIV

The invention also provides an isolated vesicle comprising one or moreHIV disease specific biomarkers, such as listed in FIG. 1 for HIVdisease. A composition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more HIV disease specific biomarkers, suchas listed in FIG. 1 for HIV disease. The composition can comprise asubstantially enriched population of vesicles, wherein the population ofvesicles is substantially homogeneous for HIV disease specific vesiclesor vesicles comprising one or more HIV disease specific biomarkers, suchas listed in FIG. 1 for HIV disease.

One or more HIV disease specific biomarkers, such as listed in FIG. 1for HIV disease, can also be detected by one or more systems disclosedherein, for characterizing a HIV disease. For example, a detectionsystem can comprise one or more probes to detect one or more HIV diseasespecific biomarkers, such as listed in FIG. 1 for HIV disease, of one ormore vesicles of a biological sample.

The one or more biomarker can also be a miRNA, such as an upregulated oroverexpressed miRNA. The upregulated miRNA can be miR-29a, miR-29b,miR-149, miR-378 or miR-324-5p. One or more biomarkers can also be usedto characterize HIV-1 latency, such as by assessing one or more miRNAs.The miRNA can be miR-28, miR-125b, miR-150, miR-223 and miR-382, andupregulated.

Asthma

The invention also provides an isolated vesicle comprising one or moreasthma disease specific biomarkers, such as listed in FIG. 1 for asthmadisease. A composition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more asthma disease specific biomarkers,such as listed in FIG. 1 for asthma disease. The composition cancomprise a substantially enriched population of vesicles, wherein thepopulation of vesicles is substantially homogeneous for asthma diseasespecific vesicles or vesicles comprising one or more asthma diseasespecific biomarkers, such as listed in FIG. 1 for asthma disease.

One or more asthma disease specific biomarkers, such as listed in FIG. 1for asthma disease, can also be detected by one or more systemsdisclosed herein, for characterizing a asthma disease. For example, adetection system can comprise one or more probes to detect one or moreasthma disease specific biomarkers, such as listed in FIG. 1 for asthmadisease, of one or more vesicles of a biological sample.

Lupus

The invention also provides an isolated vesicle comprising one or morelupus disease specific biomarkers, such as listed in FIG. 1 for lupusdisease. A composition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more lupus disease specific biomarkers,such as listed in FIG. 1 for lupus disease. The composition can comprisea substantially enriched population of vesicles, wherein the populationof vesicles is substantially homogeneous for lupus disease specificvesicles or vesicles comprising one or more lupus disease specificbiomarkers, such as listed in FIG. 1 for lupus disease.

One or more lupus disease specific biomarkers, such as listed in FIG. 1for lupus disease, can also be detected by one or more systems disclosedherein, for characterizing a lupus disease. For example, a detectionsystem can comprise one or more probes to detect one or more lupusdisease specific biomarkers, such as listed in FIG. 1 for lupus disease,of one or more vesicles of a biological sample.

Influenza

The invention also provides an isolated vesicle comprising one or moreinfluenza disease specific biomarkers, such as listed in FIG. 1 forinfluenza disease. A composition comprising the isolated vesicle is alsoprovided. Accordingly, in some embodiments, the composition comprises apopulation of vesicles comprising one or more influenza disease specificbiomarkers, such as listed in FIG. 1 for influenza disease. Thecomposition can comprise a substantially enriched population ofvesicles, wherein the population of vesicles is substantiallyhomogeneous for influenza disease specific vesicles or vesiclescomprising one or more influenza disease specific biomarkers, such aslisted in FIG. 1 for influenza disease.

One or more influenza disease specific biomarkers, such as listed inFIG. 1 for influenza disease, can also be detected by one or moresystems disclosed herein, for characterizing a influenza disease. Forexample, a detection system can comprise one or more probes to detectone or more influenza disease specific biomarkers, such as listed inFIG. 1 for influenza disease, of one or more vesicles of a biologicalsample.

Thyroid Cancer

The invention also provides an isolated vesicle comprising one or morethyroid cancer specific biomarkers, such as AKAP9-BRAF, CCDC6-RET,ERC1-RETM, GOLGA5-RET, HOOK3-RET, HRH4-RET, KTN1-RET, NCOA4-RET,PCM1-RET, PRKARA1A-RET, RFG-RET, RFG9-RET, Ria-RET, TGF-NTRK1,TPM3-NTRK1, TPM3-TPR, TPR-MET, TPR-NTRK1, TRIM24-RET, TRIM27-RET orTRIM33-RET, characteristic of papillary thyroid carcinoma; orPAX8-PPARy, characteristic of follicular thyroid cancer. A compositioncomprising the isolated vesicle is also provided. Accordingly, in someembodiments, the composition comprises a population of vesiclescomprising one or more thyroid cancer specific biomarkers, such aslisted in FIG. 1 for thyroid cancer. The composition can comprise asubstantially enriched population of vesicles, wherein the population ofvesicles is substantially homogeneous for thyroid cancer specificvesicles or vesicles comprising one or more thyroid cancer specificbiomarkers, such as listed in FIG. 1 for thyroid cancer.

One or more thyroid cancer specific biomarkers, such as listed in FIG. 1for thyroid cancer, can also be detected by one or more systemsdisclosed herein, for characterizing a thyroid cancer. For example, adetection system can comprise one or more probes to detect one or morethyroid cancer specific biomarkers, such as listed in FIG. 1 for thyroidcancer, of one or more vesicles of a biological sample.

Gene Fusions

The one or more biomarkers assessed of vesicle, can be a gene fusion,such as one or more listed in FIG. 59. A fusion gene is a hybrid genecreated by the juxtaposition of two previously separate genes. This canoccur by chromosomal translocation or inversion, deletion or viatrans-splicing. The resulting fusion gene can cause abnormal temporaland spatial expression of genes, such as leading to abnormal expressionof cell growth factors, angiogenesis factors, tumor promoters or otherfactors contributing to the neoplastic transformation of the cell andthe creation of a tumor. Such fusion genes can be oncogenic due to thejuxtaposition of: 1) a strong promoter region of one gene next to thecoding region of a cell growth factor, tumor promoter or other genepromoting oncogenesis leading to elevated gene expression, or 2) due tothe fusion of coding regions of two different genes, giving rise to achimeric gene and thus a chimeric protein with abnormal activity.

An example of a fusion gene is BCR-ABL, a characteristic molecularaberration in ˜90% of chronic myelogenous leukemia (CML) and in a subsetof acute leukemias (Kurzrock et al., Annals of Internal Medicine 2003;138(10):819-830). The BCR-ABL results from a translocation betweenchromosomes 9 and 22. The translocation brings together the 5′ region ofthe BCR gene and the 3′ region of ABL1, generating a chimeric BCR-ABL1gene, which encodes a protein with constitutively active tyrosine kinaseactivity (Mittleman et al., Nature Reviews Cancer 2007; 7(4):233-245).The aberrant tyrosine kinase activity leads to de-regulated cellsignaling, cell growth and cell survival, apoptosis resistance andgrowth factor independence, all of which contribute to thepathophysiology of leukemia (Kurzrock et al., Annals of InternalMedicine 2003; 138(10):819-830).

Another fusion gene is IGH-MYC, a defining feature of ˜80% of Burkitt'slymphoma (Ferry et al. Oncologist 2006; 11(4):375-83). The causal eventfor this is a translocation between chromosomes 8 and 14, bringing thec-Myc oncogene adjacent to the strong promoter of the immunoglobin heavychain gene, causing c-myc overexpression (Mittleman et al., NatureReviews Cancer 2007; 7(4):233-245). The c-myc rearrangement is a pivotalevent in lymphomagenesis as it results in a perpetually proliferativestate. It has wide ranging effects on progression through the cellcycle, cellular differentiation, apoptosis, and cell adhesion (Ferry etal. Oncologist 2006; 11(4):375-83).

A number of recurrent fusion genes have been catalogued in the Mittlemandatabase (cgap.nci.nih.gov/Chromosomes/Mitelman) and can be assessed ina vesicle, and used to characterize a phenotype. The gene fusion can beused to characterize a hematological malignancy or epithelial tumor. Forexample, TMPRSS2-ERG, TMPRSS2-ETV and SLC45A3-ELK4 fusions can bedetected and used to characterize prostate cancer; and ETV6-NTRK3 andODZ4-NRG1 for breast cancer.

Furthermore, assessing the presence or absence, or expression level of afusion gene can be used to diagnosis a phenotype such as a cancer aswell as a monitoring a therapeutic response to selecting a treatment.For example, the presence of the BCR-ABL fusion gene is a characteristicnot only for the diagnosis of CML, but is also the target of theNovartis drug Imatinib mesylate (Gleevec), a receptor tyrosine kinaseinhibitor, for the treatment of CML. Imatinib treatment has led tomolecular responses (disappearance of BCR-ABL+blood cells) and improvedprogression-free survival in BCR-ABL+CML patients (Kantarjian et al.,Clinical Cancer Research 2007; 13(4):1089-1097).

Assessing a vesicle for the presence, absence, or expression level of agene fusion can be of by assessing a heterogeneous population ofvesicles for the presence, absence, or expression level of a genefusion. Alternatively, the vesicle that is assessed can be derived froma specific cell type, such as cell-of-origin specific vesicle, asdescribed above. Illustrative examples of use of fusions that can beassessed to characterize a phenotype include the following:

Breast Cancer

To characterize a breast cancer, a vesicle can be assessed for one ormore breast cancer specific fusions, including, but not limited to,ETV6-NTRK3. The vesicle can be derived from a breast cancer cell.

Lung Cancer

To characterize a lung cancer, a vesicle can be assessed for one or morelung cancer specific fusions, including, but not limited to, RLF-MYCL1,TGF-ALK, or CD74-ROS1. The vesicle can be derived from a lung cancercell.

Prostate Cancer

To characterize a prostate cancer, a vesicle can be assessed for one ormore prostate cancer specific fusions, including, but not limited to,ACSL3-ETV1, C15ORF21-ETV1, FLJ35294-ETV1, HERV-ETV1,TMPRSS2-ERG,TMPRSS2-ETV1/4/5, TMPRSS2-ETV4/5, SLC5A3-ERG, SLC5A3-ETV1, SLC5A3-ETV5or KLK2-ETV4. The vesicle can be derived from a prostate cancer cell.

Brain Cancer

To characterize a brain cancer, a vesicle can be assessed for one ormore brain cancer specific fusions, including, but not limited to,GOPC-ROS 1. The vesicle can be derived from a brain cancer cell.

Head and Neck Cancer

To characterize a head and neck cancer, a vesicle can be assessed forone or more head and neck cancer specific fusions, including, but notlimited to, CHCHD7-PLAG1, CTNNB1-PLAG1, FHIT-HMGA2, HMGA2-NFIB,LIFR-PLAG1, or TCEA1-PLAG1. The vesicle can be derived from a headand/or neck cancer cell.

Renal Cell Carcinoma (RCC)

To characterize a RCC, a vesicle can be assessed for one or more RCCspecific fusions, including, but not limited to, ALPHA-TFEB, NONO-TFE3,PRCC-TFE3, SFPQ-TFE3, CLTC-TFE3, or MALAT1-TFEB. The vesicle can bederived from a RCC cell.

Thyroid Cancer

To characterize a thyroid cancer, a vesicle can be assessed for one ormore thyroid cancer specific fusions, including, but not limited to,AKAP9-BRAF, CCDC6-RET, ERC1-RETM, GOLGA5-RET, HOOK3-RET, HRH4-RET,KTN1-RET, NCOA4-RET, PCM1-RET, PRKARA1A-RET, RFG-RET, RFG9-RET, Ria-RET,TGF-NTRK1, TPM3-NTRK1, TPM3-TPR, TPR-MET, TPR-NTRK1, TRIM24-RET,TRIM27-RET or TRIM33-RET, characteristic of papillary thyroid carcinoma;or PAX8-PPARy, characteristic of follicular thyroid cancer. The vesiclecan be derived from a thyroid cancer cell.

Blood Cancers

To characterize a blood cancer, a vesicle can be assessed for one ormore blood cancer specific fusions, including, but not limited to,TTL-ETV6, CDK6-MLL, CDK6-TLX3, ETV6-FLT3, ETV6-RUNX1, ETV6-TTL,MLL-AFF1, MLL-AFF3, MLL-AFF4, MLL-GAS7, TCBA1-ETV6, TCF3-PBX1 orTCF3-TFPT, characteristic of acute lymphocytic leukemia (ALL);BCL11B-TLX3, IL2-TNFRFS17, NUP214-ABL1, NUP98-CCDC28A, TAL1-STIL, orETV6-ABL2, characteristic of T-cell acute lymphocytic leukemia (T-ALL);ATIC-ALK, KIAA1618-ALK, MSN-ALK, MYH9-ALK, NPM1-ALK, TGF-ALK orTPM3-ALK, characteristic of anaplastic large cell lymphoma (ALCL);BCR-ABL1, BCR-JAK2, ETV6-EVI1, ETV6-MN1 or ETV6-TCBA1, characteristic ofchronic myelogenous leukemia (CML); CBFB-MYH11, CHIC2-ETV6, ETV6-ABL1,ETV6-ABL2, ETV6-ARNT, ETV6-CDX2, ETV6-HLXB9, ETV6-PER1, MEF2D-DAZAP1,AML-AFF1, MLL-ARHGAP26, MLL-ARHGEF12, MLL-CASC5, MLL-CBL, MLL-CREBBP,MLL-DAB21P, MLL-ELL, MLL-EP300, MLL-EPS15, MLL-FNBP1, MLL-FOXO3A,MLL-GMPS, MLL-GPHN, MLL-MLLT1, MLL-MLLT11, MLL-MLLT3, MLL-MLLT6,MLL-MYO1F, MLL-PICALM, MLL-SEPT2, MLL-1-SEPT6, MLL-SORBS2, MYST3-SORBS2,MYST-CREBBP, NPM1-MLF1, NUP98-HOXA13, PRDM16-EVI1, RABEP1-PDGFRB,RUNX1-EVI1, RUNX1-MDS1, RUNX1-RPL22, RUNX1-RUNX1T1, RUNX1-SH3D19,RUNX1-USP42, RUNX1-YTHDF2, RUNX1-ZNF687, or TAF15-ZNF-384,characteristic of AML; CCND1-FSTL3, characteristic of chroniclymphocytic leukemia (CLL); BCL3-MYC, MYC-BTG1, BCL7A-MYC,BRWD3-ARHGAP20 or BTG1-MYC, characteristic of B-cell chronic lymphocyticleukemia (B-CLL); CITTA-BCL6, CLTC-ALK, IL21R-BCL6, PIM1-BCL6,TFCR-BCL6, IKZF1-BCL6 or SEC31A-ALK, characteristic of diffuse largeB-cell lymphomas (DLBCL); FLIP1-PDGFRA, FLT3-ETV6, KIAA1509-PDGFRA,PDE4DIP-PDGFRB, NIN-PDGFRB, TP53BP1-PDGFRB, or TPM3-PDGFRB,characteristic of hyper eosinophilia/chronic eosinophilia; IGH-MYC orLCP1-BCL6, characteristic of Burkitt's lymphoma. The vesicle can bederived from a blood cancer cell.

The invention also provides an isolated vesicle comprising one or moregene fusions as disclosed herein, such as listed in FIG. 59. Acomposition comprising the isolated vesicle is also provided.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more gene fusions, such as listed in FIG.59. The composition can comprise a substantially enriched population ofvesicles, wherein the population of vesicles is substantiallyhomogeneous for vesicles comprising one or more gene fusions, such aslisted in FIG. 59.

Also provided herein is a detection system for detecting one or moregene fusions, such as gene fusions listed in FIG. 59. For example, adetection system can comprise one or more probes to detect one or moregene fusions listed in FIG. 59. Detection of the one or more genefusions can be used to charcaterize a cancer.

Gene-Associated MiRNA Biomarkers

The one or more biomarkers assessed can also include one or more genesselected from the group consisting of PFKFB3, RHAMM (HMMR), cDNAFLJ42103, ASPM, CENPF, NCAPG, Androgen Receptor, EGFR, HSP90, SPARC,DNMT3B, GART, MGMT, SSTR3, and TOP2B. The microRNA that interacts withthe one or more genes can also be a biomarker (see for example, FIG.60). Furthermore, the one or more biomarkers can be used to characterizeprostate cancer.

The invention also provides an isolated vesicle comprising one or moreone or more biomarkers consisting of PFKFB3, RHAMM (HMMR), cDNAFLJ42103, ASPM, CENPF, NCAPG, Androgen Receptor, EGFR, HSP90, SPARC,DNMT3B, GART, MGMT, SSTR3, and TOP2B; or the microRNA that interactswith the one or more genes (see for example, FIG. 60). The inventionfurther provides a composition comprising the isolated vesicle.Accordingly, in some embodiments, the composition comprises a populationof vesicles comprising one or more biomarkers consisting of PFKFB3,RHAMM (HMMR), cDNA FLJ42103, ASPM, CENPF, NCAPG, Androgen Receptor,EGFR, HSP90, SPARC, DNMT3B, GART, MGMT, SSTR3, and TOP2B; or themicroRNA that interacts with the one or more genes, such as listed inFIG. 60. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for vesicles comprising one or more biomarkersconsisting of PFKFB3, RHAMM (HMMR), cDNA FLJ42103, ASPM, CENPF, NCAPG,Androgen Receptor, EGFR, HSP90, SPARC, DNMT3B, GART, MGMT, SSTR3, andTOP2B; or the microRNA that interacts with the one or more genes, suchas listed in FIG. 60.

One or more prostate cancer specific biomarkers, such as listed in FIG.60 can also be detected by one or more systems disclosed herein. Forexample, a detection system can comprise one or more probes to detectone or more prostate cancer specific biomarkers, such as listed in FIG.60, of one or more vesicles of a biological sample.

The miRNA that interacts with PFKFB3 can be miR-513a-3p, miR-128,miR-488, miR-539, miR-658, miR-524-5p, miR-1258, miR-150, miR-216b,miR-377, miR-135a, miR-26a, miR-548a-5p, miR-26b, miR-520d-5p, miR-224,miR-1297, miR-1197, miR-182, miR-452, miR-509-3-5p, miR-548m, miR-625,miR-509-5p, miR-1266, miR-135b, miR-190b, miR-496, miR-616, miR-621,miR-650, miR-105, miR-19a, miR-346, miR-620, miR-637, miR-651, miR-1283,miR-590-3p, miR-942, miR-1185, miR-577, miR-602, miR-1305, miR-220c,miR-1270, miR-1282, miR-432, miR-491-5p, miR-548n, miR-765, miR-768-3por miR-924, and can be used as a biomarker.

The invention also provides an isolated vesicle comprising one or moreone or more miRNA that interacts with PFKFB3. Also provided herein is acomposition comprising the isolated vesicle. Accordingly, in someembodiments, the composition comprises a population of vesiclescomprising one or more biomarkers consisting of miRNA that interactswith PFKFB3. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for vesicles comprising one or more miRNA thatinteracts with PFKFB3. Furthermore, the one or more miRNA that interactswith PFKFB3 can also be detected by one or more systems disclosedherein. For example, a detection system can comprise one or more probesto detect one or more one or more miRNA that interacts with PFKFB3 ofone or more vesicles of a biological sample.

The miRNA that interacts with RHAMM can be miR-936, miR-656, miR-105,miR-361-5p, miR-194, miR-374a, miR-590-3p, miR-186, miR-769-5p,miR-892a, miR-380, miR-875-3p, miR-208a, miR-208b, miR-586, miR-125a-3p,miR-630, miR-374b, miR-411, miR-629, miR-1286, miR-1185, miR-16,miR-200b, miR-6′71-5p, miR-95, miR-421, miR-496, miR-633, miR-1243,miR-127-5p, miR-143, miR-15b, miR-200c, miR-24 or miR-34c-3p.

The invention also provides an isolated vesicle comprising one or moreone or more miRNA that interacts with RHAMM. The invention furtherprovides a composition comprising the isolated vesicle. Accordingly, insome embodiments, the composition comprises a population of vesiclescomprising one or more biomarkers consisting of miRNA that interactswith RHAMM. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for vesicles comprising one or more miRNA thatinteracts with RHAMM. Furthermore, the one or more miRNA that interactswith RHAMM can also be detected by one or more systems disclosed herein.For example, a detection system can comprise one or more probes todetect one or more one or more miRNA that interacts with RHAMM of one ormore vesicles of a biological sample.

The miRNA that interacts with CENPF can be miR-30c, miR-30b, miR-190,miR-508-3p, miR-384, miR-512-5p, miR-548p, miR-297, miR-520f, miR-376a,miR-1184, miR-577, miR-708, miR-205, miR-376b, miR-520g, miR-520h,miR-519d, miR-596, miR-768-3p, miR-340, miR-620, miR-539, miR-567,miR-671-5p, miR-1183, miR-129-3p, miR-636, miR-106a, miR-1301, miR-17,miR-20a, miR-570, miR-656, miR-1263, miR-1324, miR-142-5p, miR-28-5p,miR-302b, miR-452, miR-520d-3p, miR-548o, miR-892b, miR-302d,miR-875-3p, miR-106b, miR-1266, miR-1323, miR-20b, miR-221, miR-520e,miR-664, miR-920, miR-922, miR-93, miR-1228, miR-1271, miR-30e,miR-483-3p, miR-509-3-5p, miR-515-3p, miR-519e, miR-520b, miR-520c-3p ormiR-582-3p.

Also provided herein is a vesicle comprising one or more one or moremiRNA that interacts with CENPF. The invention further provides acomposition comprising the isolated vesicle. Accordingly, in someembodiments, the composition comprises a population of vesiclescomprising one or more biomarkers consisting of miRNA that interactswith CENPF. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for vesicles comprising one or more miRNA thatinteracts with CENPF. Furthermore, the one or more miRNA that interactswith CENPF can also be detected by one or more systems disclosed herein.For example, a detection system can comprise one or more probes todetect one or more one or more miRNA that interacts with CENPF of one ormore vesicles of a biological sample.

The miRNA that interacts with NCAPG can be miR-876-5p, miR-1260,miR-1246, miR-548c-3p, miR-1224-3p, miR-619, miR-605, miR-490-5p,miR-186, miR-448, miR-129-5p, miR-188-3p, miR-516b, miR-342-3p,miR-1270, miR-548k, miR-654-3p, miR-1290, miR-656, miR-34b, miR-520g,miR-1231, miR-1289, miR-1229, miR-23a, miR-23b, miR-616 or miR-620.

The invention also provides an isolated vesicle comprising one or moreone or more miRNA that interacts with NCAPG. The invention furtherprovides a composition comprising the isolated vesicle. Accordingly, insome embodiments, the composition comprises a population of vesiclescomprising one or more biomarkers consisting of miRNA that interactswith NCAPG. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for vesicles comprising one or more miRNA thatinteracts with NCAPG. Furthermore, the one or more miRNA that interactswith NCAPG can also be detected by one or more systems disclosed herein.For example, a detection system can comprise one or more probes todetect one or more one or more miRNA that interacts with NCAPG of one ormore vesicles of a biological sample.

, The miRNA that interacts with Androgen Receptor can be miR-124a,miR-130a, miR-130b, miR-143, miR-149, miR-194, miR-29b, miR-29c,miR-301, miR-30a-5p, miR-30d, miR-30e-5p, miR-337, miR-342, miR-368,miR-488, miR-493-5p, miR-506, miR-512-5p, miR-644, miR-768-5p ormiR-801.

The miRNA that interacts with EGFR can be miR-105, miR-128a, miR-128b,miR-140, miR-141, miR-146a, miR-146b, miR-27a, miR-27b, miR-302a,miR-302d, miR-370, miR-548c, miR-574, miR-587 or miR-7.

The invention also provides an isolated vesicle, comprising one or moreone or more miRNA that interacts with AR. The invention further providesa composition comprising the isolated vesicle. Accordingly, in someembodiments, the composition comprises a population of vesiclescomprising one or more biomarkers consisting of miRNA that interactswith AR. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for vesicles comprising one or more miRNA thatinteracts with AR. Furthermore, the one or more miRNA that interactswith AR can also be detected by one or more systems disclosed herein.For example, a detection system can comprise one or more probes todetect one or more one or more miRNA that interacts with AR of one ormore vesicles of a biological sample.

The miRNA that interacts with HSP90 can be miR-1, miR-513a-3p,miR-548d-3p, miR-642, miR-206, miR-450b-3p, miR-152, miR-148a, miR-148b,miR-188-3p, miR-23a, miR-23b, miR-578, miR-653, miR-1206, miR-192,miR-215, miR-181b, miR-181d, miR-223, miR-613, miR-769-3p, miR-99a,miR-100, miR-454, miR-548n, miR-640, miR-99b, miR-150, miR-181a,miR-181c, miR-522, miR-624, miR-130a, miR-130b, miR-146, miR-148a,miR-148b, miR-152, miR-181a, miR-181b, miR-181c, miR-204, miR-206,miR-211, miR-212, miR-215, miR-223, miR-23a, miR-23b, miR-301, miR-31,miR-325, miR-363, miR-566, miR-9 or miR-99b.

The invention also provides an isolated vesicle, comprising one or moreone or more miRNA that interacts with HSP90. The invention furtherprovides a composition comprising the isolated vesicle. Accordingly, insome embodiments, the composition comprises a population of vesiclescomprising one or more biomarkers consisting of miRNA that interactswith HSP90. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for vesicles comprising one or more miRNA thatinteracts with HSP90. Furthermore, the one or more miRNA that interactswith HSP90 can also be detected by one or more systems disclosed herein.For example, a detection system can comprise one or more probes todetect one or more one or more miRNA that interacts with HSP90 of one ormore vesicles of a biological sample.

The miRNA that interacts with SPARC can be miR-768-5p, miR-203,miR-196a, miR-569, miR-187, miR-641, miR-1275, miR-432, miR-622,miR-296-3p, miR-646, miR-196b, miR-499-5p, miR-590-5p, miR-495, miR-625,miR-1244, miR-512-5p, miR-1206, miR-1303, miR-186, miR-302d, miR-494,miR-562, miR-573, miR-10a, miR-203, miR-204, miR-211, miR-29, miR-29b,miR-29c, miR-339, miR-433, miR-452, miR-515-5p, miR-517a, miR-517b,miR-517c, miR-592 or miR-96.

The invention also provides an isolated vesicle comprising one or moreone or more miRNA that interacts with SPARC. The invention furtherprovides a composition comprising the isolated vesicle. Accordingly, insome embodiments, the composition comprises a population of vesiclescomprising one or more biomarkers consisting of miRNA that interactswith SPARC. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for vesicles comprising one or more miRNA thatinteracts with SPARC. Furthermore, the one or more miRNA that interactswith SPARC can also be detected by one or more systems disclosed herein.For example, a detection system can comprise one or more probes todetect one or more one or more miRNA that interacts with SPARC of one ormore vesicles of a biological sample.

The miRNA that interacts with DNMT3B can be miR-618, miR-1253, miR-765,miR-561, miR-330-5p, miR-326, miR-188, miR-203, miR-221, miR-222,miR-26a, miR-26b, miR-29a, miR-29b, miR-29c, miR-370, miR-379, miR-429,miR-519e, miR-598, miR-618 or miR-635.

The invention also provides an isolated vesicle comprising one or moreone or more miRNA that interacts with DNMT3B. The invention furtherprovides a composition comprising the isolated vesicle. Accordingly, insome embodiments, the composition comprises a population of vesiclescomprising one or more biomarkers consisting of miRNA that interactswith DNMT3B. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for vesicles comprising one or more miRNA thatinteracts with DNMT3B. Furthermore, the one or more miRNA that interactswith DNMT3B can also be detected by one or more systems disclosedherein. For example, a detection system can comprise one or more probesto detect one or more one or more miRNA that interacts with DNMT3B ofone or more vesicles of a biological sample.

The miRNA that interacts with GART can be miR-101, miR-141, miR-144,miR-182, miR-189, miR-199a, miR-199b, miR-200a, miR-200b, miR-202,miR-203, miR-223, miR-329, miR-383, miR-429, miR-433, miR-485-5p,miR-493-5p, miR-499, miR-519a, miR-519b, miR-519c, miR-569, miR-591,miR-607, miR-627, miR-635, miR-636 or miR-659.

The invention also provides an isolated vesicle comprising one or moreone or more miRNA that interacts with GART. The invention furtherprovides a composition comprising the isolated vesicle. Accordingly, insome embodiments, the composition comprises a population of vesiclescomprising one or more biomarkers consisting of miRNA that interactswith GART. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for vesicles comprising one or more miRNA thatinteracts with GART. Furthermore, the one or more miRNA that interactswith GART can also be detected by one or more systems disclosed herein.For example, a detection system can comprise one or more probes todetect one or more one or more miRNA that interacts with GART of one ormore vesicles of a biological sample.

The miRNA that interacts with MGMT can be miR-122a, miR-142-3p,miR-17-3p, miR-181a, miR-181b, miR-181c, miR-181d, miR-199b, miR-200a,miR-217, miR-302b, miR-32, miR-324-3p, miR-34a, miR-371, miR-425-5p,miR-496, miR-514, miR-515-3p, miR-516-3p, miR-574, miR-597, miR-603,miR-653, miR-655, miR-92, miR-92b or miR-99a.

The invention also provides an isolated vesicle comprising one or moreone or more miRNA that interacts with MGMT. The invention furtherprovides a composition comprising the isolated vesicle. Accordingly, insome embodiments, the composition comprises a population of vesiclescomprising one or more biomarkers consisting of miRNA that interactswith MGMT. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for vesicles comprising one or more miRNA thatinteracts with MGMT. Furthermore, the one or more miRNA that interactswith MGMT can also be detected by one or more systems disclosed herein.For example, a detection system can comprise one or more probes todetect one or more one or more miRNA that interacts with MGMT of one ormore vesicles of a biological sample.

The miRNA that interacts with SSTR3 can be miR-125a, miR-125b, miR-133a,miR-133b, miR-136, miR-150, miR-21, miR-380-5p, miR-504, miR-550,miR-671, miR-766 or miR-767-3p.

The invention also provides an isolated vesicle comprising one or moreone or more miRNA that interacts with SSTR3. The invention furtherprovides a composition comprising the isolated vesicle. Accordingly, insome embodiments, the composition comprises a population of vesiclescomprising one or more biomarkers consisting of miRNA that interactswith SSTR3. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for vesicles comprising one or more miRNA thatinteracts with SSTR3. Furthermore, the one or more miRNA that interactswith SSTR3 can also be detected by one or more systems disclosed herein.For example, a detection system can comprise one or more probes todetect one or more one or more miRNA that interacts with SSTR3 of one ormore vesicles of a biological sample.

The miRNA that interacts with TOP2B can be miR-548f, miR-548a-3p,miR-548g, miR-513a-3p, miR-548c-3p, miR-101, miR-653, miR-548d-3p,miR-575, miR-297, miR-576-3p, miR-548b-3p, miR-624, miR-548n, miR-758,miR-1253, miR-1324, miR-23b, miR-320a, miR-320b, miR-1183, miR-1244,miR-23a, miR-451, miR-568, miR-1276, miR-548e, miR-590-3p, miR-1,miR-101, miR-126, miR-129, miR-136, miR-140, miR-141, miR-144, miR-147,miR-149, miR-18, miR-181b, miR-181c, miR-182, miR-184, miR-186, miR-189,miR-191, miR-19a, miR-19b, miR-200a, miR-206, miR-210, miR-218, miR-223,miR-23a, miR-23b, miR-24, miR-27a, miR-302, miR-30a, miR-31, miR-320,miR-323, miR-362, miR-374, miR-383, miR-409-3p, miR-451, miR-489,miR-493-3p, miR-514, miR-542-3p, miR-544, miR-548a, miR-548b, miR-548c,miR-548d, miR-559, miR-568, miR-575, miR-579, miR-585, miR-591, miR-598,miR-613, miR-649, miR-651, miR-758, miR-768-3p or miR-9.

Also provided herein is a vesicle comprising one or more one or moremiRNA that interacts with TOP2B. The invention further provides acomposition comprising the isolated vesicle. Accordingly, in someembodiments, the composition comprises a population of vesiclescomprising one or more biomarkers consisting of miRNA that interactswith TOP2B. The composition can comprise a substantially enrichedpopulation of vesicles, wherein the population of vesicles issubstantially homogeneous for vesicles comprising one or more miRNA thatinteracts with TOP2B. Furthermore, the one or more miRNA that interactswith TOP2B can also be detected by one or more systems disclosed herein.For example, a detection system can comprise one or more probes todetect one or more one or more miRNA that interacts with TOP2B of one ormore vesicles of a biological sample.

Other MicroRNA Biomarkers

Other microRNAs that can be detected or assessed in a vesicle and usedto characterize a phenotype include, but are not limited to, hsa-let-7a,hsa-let-7b, hsa-let-7c, hsa-let-7d, hsa-let-7e, hsa-let-7f, hsa-miR-15a,hsa-miR-16, hsa-miR-17-5p, hsa-miR-17-3p, hsa-miR-18a, hsa-miR-19a,hsa-miR-19b, hsa-miR-20a, hsa-miR-21, hsa-miR-22, hsa-miR-23a,hsa-miR-189, hsa-miR-24, hsa-miR-25, hsa-miR-26a, hsa-miR-26b,hsa-miR-27a, hsa-miR-28, hsa-miR-29a, hsa-miR-30a-5p, hsa-miR-30a-3p,hsa-miR-31, hsa-miR-32, hsa-miR-33, hsa-miR-92, hsa-miR-93, hsa-miR-95,hsa-miR-96, hsa-miR-98, hsa-miR-99a, hsa-miR-100, hsa-miR-101,hsa-miR-29b, hsa-miR-103, hsa-miR-105, hsa-miR-106a, hsa-miR-107,hsa-miR-192, hsa-miR-196a, hsa-miR-197, hsa-miR-198, hsa-miR-199a,hsa-miR-199a*, hsa-miR-208, hsa-miR-129, hsa-miR-148a, hsa-miR-30c,hsa-miR-30d, hsa-miR-139, hsa-miR-147, hsa-miR-7, hsa-miR-10a,hsa-miR-10b, hsa-miR-34a, hsa-miR-181a, hsa-miR-181b, hsa-miR-181c,hsa-miR-182, hsa-miR-182*, hsa-miR-183, hsa-miR-187, hsa-miR-199b,hsa-miR-203, hsa-miR-204, hsa-miR-205, hsa-miR-210, hsa-miR-211,hsa-miR-212, hsa-miR-181a*, hsa-miR-214, hsa-miR-215, hsa-miR-216,hsa-miR-217, hsa-miR-218, hsa-miR-219, hsa-miR-220, hsa-miR-221,hsa-miR-222, hsa-miR-223, hsa-miR-224, hsa-miR-200b, hsa-let-7g,hsa-let-7i, hsa-miR-1, hsa-miR-15b, hsa-miR-23b, hsa-miR-27b,hsa-miR-30b, hsa-miR-122a, hsa-miR-124a, hsa-miR-125b, hsa-miR-128a,hsa-miR-130a, hsa-miR-132, hsa-miR-133a, hsa-miR-135a, hsa-miR-137,hsa-miR-138, hsa-miR-140, hsa-miR-141, hsa-miR-142-5p, hsa-miR-142-3p,hsa-miR-143, hsa-miR-144, hsa-miR-145, hsa-miR-152, hsa-miR-153,hsa-miR-191, hsa-miR-9, hsa-miR-9*, hsa-miR-125a, hsa-miR-126*,hsa-miR-126, hsa-miR-127, hsa-miR-134, hsa-miR-136, hsa-miR-146a,hsa-miR-149, hsa-miR-150, hsa-miR-154, hsa-miR-154*, hsa-miR-184,hsa-miR-185, hsa-miR-186, hsa-miR-188, hsa-miR-190, hsa-miR-193a,hsa-miR-194, hsa-miR-195, hsa-miR-206, hsa-miR-320, hsa-miR-200c,hsa-miR-155, hsa-miR-128b, hsa-miR-106b, hsa-miR-29c, hsa-miR-200a,hsa-miR-302a*, hsa-miR-302a, hsa-miR-34b, hsa-miR-34c, hsa-miR-299-3p,hsa-miR-301, hsa-miR-99b, hsa-miR-296, hsa-miR-130b, hsa-miR-30e-5p,hsa-miR-30e-3p, hsa-miR-361, hsa-miR-362, hsa-miR-363, hsa-miR-365,hsa-mir-302b*, hsa-miR-302b, hsa-miR-302c*, hsa-miR-302c, hsa-miR-302d,hsa-miR-367, hsa-miR-368, hsa-miR-369-3p, hsa-miR-370, hsa-miR-371,hsa-miR-372, hsa-miR-373*, hsa-miR-373, hsa-miR-374, hsa-miR-375,hsa-miR-376a, hsa-miR-377, hsa-miR-378, hsa-miR-422b, hsa-miR-379,hsa-miR-380-5p, hsa-miR-380-3p, hsa-miR-381, hsa-miR-382, hsa-miR-383,hsa-miR-340, hsa-miR-330, hsa-miR-328, hsa-miR-342, hsa-miR-337,hsa-miR-323, hsa-miR-326, hsa-miR-151, hsa-miR-135b, hsa-miR-148b,hsa-miR-331, hsa-miR-324-5p, hsa-miR-324-3p, hsa-miR-338, hsa-miR-339,hsa-miR-335, hsa-miR-133b, hsa-miR-325, hsa-miR-345, hsa-miR-346,ebv-miR-BHRF1-1, ebv-miR-BHRF1-2*, ebv-miR-BHRF1-2, ebv-miR-BHRF1-3,ebv-miR-BART1-5p, ebv-miR-BART2, hsa-miR-384, hsa-miR-196b,hsa-miR-422a, hsa-miR-423, hsa-miR-424, hsa-miR-425-3p, hsa-miR-18b,hsa-miR-20b, hsa-miR-448, hsa-miR-429, hsa-miR-449, hsa-miR-450,hcmv-miR-UL22A, hcmv-miR-UL22A*, hcmv-miR-UL36, hcmv-miR-UL112,hcmv-miR-UL148D, hcmv-miR-US5-1, hcmv-miR-US5-2, hcmv-miR-US25-1,hcmv-miR-US25-2-5p, hcmv-miR-US25-2-3p, hcmv-miR-US33, hsa-miR-191*,hsa-miR-200a*, hsa-miR-369-5p, hsa-miR-431, hsa-miR-433, hsa-miR-329,hsa-miR-453, hsa-miR-451, hsa-miR-452, hsa-miR-452*, hsa-miR-409-5p,hsa-miR-409-3p, hsa-miR-412, hsa-miR-410, hsa-miR-376b, hsa-miR-483,hsa-miR-484, hsa-miR-485-5p, hsa-miR-485-3p, hsa-miR-486, hsa-miR-487a,kshv-miR-K12-10a, kshv-miR-K12-10b, kshv-miR-K12-11, kshv-miR-K12-1,kshv-miR-K12-2, kshv-miR-K12-9*, kshv-miR-K12-9, kshv-miR-K12-8,kshv-miR-K12-7, kshv-miR-K12-6-5p, kshv-miR-K12-6-3p, kshv-miR-K12-5,kshv-miR-K12-4-5p, kshv-miR-K12-4-3p, kshv-miR-K12-3, kshv-miR-K12-3*,hsa-miR-488, hsa-miR-489, hsa-miR-490, hsa-miR-491, hsa-miR-511,hsa-miR-146b, hsa-miR-202*, hsa-miR-202, hsa-miR-492, hsa-miR-493-5p,hsa-miR-432, hsa-miR-432*, hsa-miR-494, hsa-miR-495, hsa-miR-496,hsa-miR-193b, hsa-miR-497, hsa-miR-181d, hsa-miR-512-5p, hsa-miR-512-3p,hsa-miR-498, hsa-miR-520e, hsa-miR-515-5p, hsa-miR-515-3p,hsa-miR-519e*, hsa-miR-519e, hsa-miR-520f, hsa-miR-526c, hsa-miR-519c,hsa-miR-520a*, hsa-miR-520a, hsa-miR-526b, hsa-miR-526b*, hsa-miR-519b,hsa-miR-525, hsa-miR-525*, hsa-miR-523, hsa-miR-518% hsa-miR-518f,hsa-miR-520b, hsa-miR-518b, hsa-miR-526a, hsa-miR-520c, hsa-miR-518c*,hsa-miR-518c, hsa-miR-524*, hsa-miR-524, hsa-miR-517*, hsa-miR-517a,hsa-miR-519d, hsa-miR-521, hsa-miR-520d*, hsa-miR-520d, hsa-miR-517b,hsa-miR-520g, hsa-miR-516-5p, hsa-miR-516-3p, hsa-miR-518e, hsa-miR-527,hsa-miR-518a, hsa-miR-518d, hsa-miR-517c, hsa-miR-520h, hsa-miR-522,hsa-miR-519a, hsa-miR-499, hsa-miR-500, hsa-miR-501, hsa-miR-502,hsa-miR-503, hsa-miR-504, hsa-miR-505, hsa-miR-513, hsa-miR-506,hsa-miR-507, hsa-miR-508, hsa-miR-509, hsa-miR-510, hsa-miR-514,hsa-miR-532, hsa-miR-299-5p, hsa-miR-18a*, hsa-miR-455, hsa-miR-493-3p,hsa-miR-539, hsa-miR-544, hsa-miR-545, hsa-miR-487b, hsa-miR-551a,hsa-miR-552, hsa-miR-553, hsa-miR-554, hsa-miR-92b, hsa-miR-555,hsa-miR-556, hsa-miR-557, hsa-miR-558, hsa-miR-559, hsa-miR-560,hsa-miR-561, hsa-miR-562, hsa-miR-563, hsa-miR-564, hsa-miR-565,hsa-miR-566, hsa-miR-567, hsa-miR-568, hsa-miR-551b, hsa-miR-569,hsa-miR-570, hsa-miR-571, hsa-miR-572, hsa-miR-573, hsa-miR-574,hsa-miR-575, hsa-miR-576, hsa-miR-577, hsa-miR-578, hsa-miR-579,hsa-miR-580, hsa-miR-581, hsa-miR-582, hsa-miR-583, hsa-miR-584,hsa-miR-585, hsa-miR-548a, hsa-miR-586, hsa-miR-587, hsa-miR-548b,hsa-miR-588, hsa-miR-589, hsa-miR-550, hsa-miR-590, hsa-miR-591,hsa-miR-592, hsa-miR-593, hsa-miR-595, hsa-miR-596, hsa-miR-597,hsa-miR-598, hsa-miR-599, hsa-miR-600, hsa-miR-601, hsa-miR-602,hsa-miR-603, hsa-miR-604, hsa-miR-605, hsa-miR-606, hsa-miR-607,hsa-miR-608, hsa-miR-609, hsa-miR-610, hsa-miR-611, hsa-miR-612,hsa-miR-613, hsa-miR-614, hsa-miR-615, hsa-miR-616, hsa-miR-548c,hsa-miR-617, hsa-miR-618, hsa-miR-619, hsa-miR-620, hsa-miR-621,hsa-miR-622, hsa-miR-623, hsa-miR-624, hsa-miR-625, hsa-miR-626,hsa-miR-627, hsa-miR-628, hsa-miR-629, hsa-miR-630, hsa-miR-631,hsa-miR-33b, hsa-miR-632, hsa-miR-633, hsa-miR-634, hsa-miR-635,hsa-miR-636, hsa-miR-637, hsa-miR-638, hsa-miR-639, hsa-miR-640,hsa-miR-641, hsa-miR-642, hsa-miR-643, hsa-miR-644, hsa-miR-645,hsa-miR-646, hsa-miR-647, hsa-miR-648, hsa-miR-649, hsa-miR-650,hsa-miR-651, hsa-miR-652, hsa-miR-548d, hsa-miR-661, hsa-miR-662,hsa-miR-663, hsa-miR-449b, hsa-miR-653, hsa-miR-411, hsa-miR-654,hsa-miR-655, hsa-miR-656, hsa-miR-549, hsa-miR-657, hsa-miR-658,hsa-miR-659, hsa-miR-660, hsa-miR-421, hsa-miR-542-5p, hcmv-miR-US4,hcmv-miR-UL70-5p, hcmv-miR-UL70-3p, hsa-miR-363*, hsa-miR-376a*,hsa-miR-542-3p, ebv-miR-BART1-3p, hsa-miR-425-5p, ebv-miR-BART3-5p,ebv-miR-BART3-3p, ebv-miR-BART4, ebv-miR-BART5, ebv-miR-BART6-5p,ebv-miR-BART6-3p, ebv-miR-BART7, ebv-miR-BART8-5p, ebv-miR-BART8-3p,ebv-miR-BART9, ebv-miR-BART10, ebv-miR-BART11-5p, ebv-miR-BART11-3p,ebv-miR-BART12, ebv-miR-BART13, ebv-miR-BART14-5p, ebv-miR-BART14-3p,kshv-miR-K12-12, ebv-miR-BART15, ebv-miR-BART16, ebv-miR-BART17-5p,ebv-miR-BART17-3p, ebv-miR-BART18, ebv-miR-BART19, ebv-miR-BART20-5p,ebv-miR-BART20-3p, hsv1-miR-H1, hsa-miR-758, hsa-miR-671, hsa-miR-668,hsa-miR-767-5p, hsa-miR-767-3p, hsa-miR-454-5p, hsa-miR-454-3p,hsa-miR-769-5p, hsa-miR-769-3p, hsa-miR-766, hsa-miR-765,hsa-miR-768-5p, hsa-miR-768-3p, hsa-miR-770-5p, hsa-miR-802,hsa-miR-801, and hsa-miR-675.

For example, without being bound by theory, miR-128A5 miR-129 andmiR-128B are highly enriched in brain; miR-194, miR-148 and miR-192 arehighly enriched in liver; mIR-96, miR-150, miR-205, miR-182 and miR-183are highly enriched in the thymus; miR-204, miR-10B5 miR-154 and miR134are highly enriched in testes; and miR-122, miR-210, miR-221, miR-141,miR-23A, miR-200C and miR-136 are highly enriched in the placenta. Thebiosignature comprising one or more of the aforementioned miRs can beused to distinguish positive and negative lymph nodes from a subjectwith cervical, colon or breast cancer.

In another embodiment, a biosignature can comprise one or more of thefollowing miRs: miR-125b-1, miR125b-2, miR-145, miR-21, miR-155,miR-10b, miR-009-1 (miR131-1), miR-34 (miR-170), miR-102 (miR-29b),miR-123 (miR-126), miR-140-as, miR-125a, miR-125b-1, miR-125b-2,miR-194, miR-204, miR-213, let-7a-2, let-7a-3, let-7d (let-7d-v1),let-7f-2, let-71 (let-7d-v2), miR-101-1, miR-122a, miR-128b, miR-136,miR-143, miR-149, miR-191, miR-196-1, miR-196-2, miR-202, miR-203,miR-206, and miR-210, which can be used to characterize breast cancer.

In another embodiment, miR-375 expression is detected in a vesicle andused to characterize pancreatic insular or acinar tumors.

In yet another embodiment, one or more of the following miRs can bedetected in a vesicle: miR-103-2, miR-107, miR-103-1, miR-342, miR-100,miR-24-2, miR-23a, miR-125a, miR-26a-1, miR-24-1, miR-191, miR-15a,miR-368, miR-26b, miR-125b-2, miR-125b-1, miR-26a-2, miR-335, miR-126.miR-1-2, miR-21, miR-25, miR-92-2, miR-130a, miR-93, miR-16-1, miR-145,miR-17, miR-99b, miR-181b-1, miR-146, miR-181b-2, miR-16-2, miR-99a,miR-197, miR-10a, miR-224, miR-92-1, miR-27a, miR-221, miR-320, miR-7-1,miR-29b-2, miR-150, miR-30d, miR-29a, miR-23b, miR-135a-2, miR-223,miR-3p21-v, miR-128b, miR-30b, miR-29b-1, miR-106b, miR-132, miR-214,miR-7-3, miR-29c, miR-367, miR-30c-2, miR-27b, miR-140, miR-10b, miR-20,miR-129-1, miR-340, miR-30a, miR-30c-1, miR-106a, miR-32, miR-95,miR-222, miR-30e, miR-129-2, miR-345, miR-143, miR-182, miR-1-1,miR-133a-1, miR-200c, miR-194-1, miR-210, miR-181c, miR-192, miR-220,miR-213, miR-323, and miR-375, wherein high expression or overexpressionof the one or more miRs can be used to characterize pancreatic cancer.

Expression of one or more of the following miRs: miR-101, miR-126,miR-99a, miR-99-prec, miR-106, miR-339, miR-99b, miR-149, miR-33,miR-135 and miR-20 can be detected in a vesicle and used to characterizemegakaryocytopoiesis.

It is believed cell proliferation has been correlated with theexpression of miR-31, miR-92, miR-99a, miR-100, miR-125a, miR-129,miR-130a, miR-150, miR-187, miR-190, miR-191, miR-193, miR 204, miR-210,miR-21 1, miR-212, miR-213, miR-215, miR-216, miR-217, miR 218, miR-224,miR-292, miR-294, miR-320, miR-324, miR-325, miR-326, miR-330, miR-331,miR-338, miR-341, miR-369, miR-370, et-7a, Let-7b, Let-7c, Let-7d,Let-7g, miR-7, miR-9, miR-10a, miR-10b, miR-15a, miR-18, miR-19a,miR-17-3p, miR-20, miR-23b, miR-25, miR-26a, miR-26a, miR-30e-5p,miR-31, miR-32, miR-92, miR-93, miR-100, miR-125a, miR-125b, miR-126,miR-127, miR-128, miR-129, miR-130a, miR-135, miR-138, miR-139, miR-140,miR-141, miR-143, miR-145, miR-146, miR-150, miR-154, miR-155, miR-181a,miR-182, miR-186, miR-187, miR-188, miR-190, miR-191, miR-193, miR-194,miR-196, miR-197, miR-198, miR-199, miR-201, miR-204, miR-216, miR-218,miR-223, miR-293, miR-291-3p, miR-294, miR-295, miR-322, miR-333,miR-335, miR-338, miR-341, miR-350, miR-369, miR-373, miR-410, andmiR-412. Detection one or more of the above miRs can be used tocharacterize a cancer.

Other examples of miRs that detected in a vesicle and used tocharacterize cancer is disclosed in U.S. Pat. No. 7,642,348, describingidentification of 3,765 unique nucleic acid sequences correlated withprostate cancer), and U.S. Pat. No. 7,592,441, which describes microRNAsrelated to liver cancer.

Other microRNAs that are expressed commonly in solid cancer, such ascolon cancer, lung cancer, breast cancer, stomach cancer, prostatecancer, and pancreatic cancer, can also be detected in a vesicle andused to characterize a cancer. For example, one or more of the followingmiRs: miR-21, miR-17-5p, miR-191, miR-29b-2, miR-223, miR-128b,miR-199a-1, miR-24-1, miR-24-2, miR-146, miR-155, miR-181b-1, miR-20a,miR-107, miR-32, miR-92-2, miR-214, miR-30c, miR-25, miR-221, andmiR-106a, can be detected in a vesicle and used to characterize a solidcancer.

Other examples of microRNAs that can be detected in a vesicle aredisclosed in PCT Publication Nos. WO2006126040, WO2006033020,WO2005116250, and WO2005111211, US Publications Nos. US20070042982 andUS20080318210; and EP Publication Nos. EP1784501A2 and EP1751311A2, eachof which is incorporated by reference.

Biomarker Detection

A biosignature can be detected qualitatively or quantitatively bydetecting a presence, level or concentration of a microRNA, vesicle orother biomarkers, as disclosed herein. These biosignature components canbe detected using a number of techniques known to those of skill in theart. For example, a biomarker can be detected by microarray analysis,polymerase chain reaction (PCR) (including PCR-based methods such asreal time polymerase chain reaction (RT-PCR), quantitative real timepolymerase chain reaction (Q-PCR/qPCR) and the like), hybridization withallele-specific probes, enzymatic mutation detection, ligation chainreaction (LCR), oligonucleotide ligation assay (OLA), flow-cytometricheteroduplex analysis, chemical cleavage of mismatches, massspectrometry, nucleic acid sequencing, single strand conformationpolymorphism (SSCP), denaturing gradient gel electrophoresis (DGGE),temperature gradient gel electrophoresis (TGGE), restriction fragmentpolymorphisms, serial analysis of gene expression (SAGE), orcombinations thereof. A biomarker, such as a nucleic acid, can beamplified prior to detection. A biomarker can also be detected byimmunoassay, immunoblot, immunoprecipitation, enzyme-linkedimmunosorbent assay (ELISA; EIA), radioimmunoassay (RIA), flowcytometry, or electron microscopy (EM).

Biosignatures can be detected using capture agents and detection agents,as described herein. A capture agent can comprise an antibody, aptameror other entity which recognizes a biomarker and can be used forcapturing the biomarker. Biomarkers that can be captured includecirculating biomarkers, e.g., a protein, nucleic acid, lipid orbiological complex in solution in a bodily fluid. Similarly, the captureagent can be used for capturing a vesicle. A detection agent cancomprise an antibody or other entity which recognizes a biomarker andcan be used for detecting the biomarker vesicle, or which recognizes avesicle and is useful for detecting a vesicle. In some embodiments, thedetection agent is labeled and the label is detected, thereby detectingthe biomarker or vesicle. The detection agent can be a binding agent,e.g., an antibody or aptamer. In other embodiments, the detection agentcomprises a small molecule such as a membrane protein labeling agent.See, e.g., the membrane protein labeling agents disclosed in Alroy etal., US. Patent Publication US 2005/0158708. In an embodiment, vesiclesare isolated or captured as described herein, and one or more membraneprotein labeling agent is used to detect the vesicles. In many cases,the antigen or other vesicle-moiety that is recognized by the captureand detection agents are interchangeable. As a non-limiting example,consider a vesicle having a cell-of-origin specific antigen on itssurface and a cancer-specific antigen on its surface. In one instance,the vesicle can be captured using an antibody to the cell-of-originspecific antigen, e.g., by tethering the capture antibody to asubstrate, and then the vesicle is detected using an antibody to thecancer-specific antigen, e.g., by labeling the detection antibody with afluorescent dye and detecting the fluorescent radiation emitted by thedye. In another instance, the vesicle can be captured using an antibodyto the cancer specific antigen, e.g., by tethering the capture antibodyto a substrate, and then the vesicle is detected using an antibody tothe cell-of-origin specific antigen, e.g., by labeling the detectionantibody with a fluorescent dye and detecting the fluorescent radiationemitted by the dye.

In some embodiments, a same biomarker is recognized by both a captureagent and a detection agent. This scheme can be used depending on thesetting. In one embodiment, the biomarker is sufficient to detect avesicle of interest, e.g., to capture cell-of-origin specific vesicles.In other embodiments, the biomarker is multifunctional, e.g., havingboth cell-of-origin specific and cancer specific properties. Thebiomarker can be used in concert with other biomarkers for capture anddetection as well.

One method of detecting a biomarker comprises purifying or isolating aheterogeneous population of vesicles from a biological sample, asdescribed above, and performing a sandwich assay. A vesicle in thepopulation can be captured with a capture agent. The capture agent canbe a capture antibody, such as a primary antibody. The capture antibodycan be bound to a substrate, for example an array, well, or particle.The captured or bound vesicle can be detected with a detection agent,such as a detection antibody. For example, the detection antibody can befor an antigen of the vesicle. The detection antibody can be directlylabeled and detected. Alternatively, the detection agent can beindirectly labeled and detected, such as through an enzyme linkedsecondary antibody that can react with the detection agent. A detectionreagent or detection substrate can be added and the reaction detected,such as described in PCT Publication No. WO2009092386. In anillustrative example wherein the capture agent binds Rab-5b and thedetection agent binds or detects CD63 or caveolin-1, the capture agentcan be an anti-Rab 5b antibody and the detection agent can be ananti-CD63 or anti-caveolin-1 antibody. In some embodiments, the captureagent binds CD9, PSCA, TNFR, CD63, B7H3, MFG-E8, EpCam, Rab, CD81,STEAP, PCSA, PSMA, or 5T4. For example, the capture agent can be anantibody to CD9, PSCA, TNFR, CD63, B7H3, MFG-E8, EpCam, Rab, CD81,STEAP, PCSA, PSMA, or 5T4. The capture agent can also be an antibody toMFG-E8, Annexin V, Tissue Factor, DR3, STEAP, epha2, TMEM211, unc93A,A33, CD24, NGAL, EpCam, MUC17, TROP2, or TETS. The detection agent canbe an agent that binds or detects CD63, CD9, CD81, B7H3, or EpCam, suchas a detection antibody to CD63, CD9, CD81, B7H3, or EpCam. Variouscombinations of capture and/or detection agents can be used in concert.In an embodiment, the capture agents comprise PCSA, PSMA, B7H3 andoptionally EpCam, and the detection agents comprise one or moretetraspanin such as CD9, CD63 and CD81. In another embodiment, thecapture agents comprise TMEM211 and CD24, and the detection agentscomprise one or more tetraspanin such as CD9, CD63 and CD81. In anotherembodiment, the capture agents comprise CD66 and EpCam, and thedetection agents comprise one or more tetraspanin such as CD9, CD63 andCD81. Increasing numbers of such tetraspanins and/or other generalvesicle markers can improve the detection signal in some cases. Proteinsor other circulating biomarkers can also be detected using sandwichapproaches. The captured vesicles can be collected and used to analyzethe payload contained therein, e.g., mRNA, microRNAs, DNA and solubleprotein.

In some embodiments, the capture agent binds or targets EpCam, B7H3 orCD24, and the one or more biomarkers detected on the vesicle are CD9and/or CD63. In one embodiment, the capture agent binds or targetsEpCam, and the one or more biomarkers detected on the vesicle are CD9,EpCam and/or CD81. The single capture agent can be selected from CD9,PSCA, TNFR, CD63, B7H3, MFG-E8, EpCam, Rab, CD81, STEAP, PCSA, PSMA, or5T4. The single capture agent can also be an antibody to DR3, STEAP,epha2, TMEM211, unc93A, A33, CD24, NGAL, EpCam, MUC17, TROP2, MFG-E8,TF, Annexin V or TETS. In some embodiments, the single capture agent isselected from PCSA, PSMA, B7H3, CD81, CD9 and CD63.

In other embodiments, the capture agent targets PCSA, and the one ormore biomarkers detected on the captured vesicle are B7H3 and/or PSMA.In other embodiments, the capture agent targets PSMA, and the one ormore biomarkers detected on the captured vesicle are B7H3 and/or PCSA.In other embodiments, the capture agent targets B7H3, and the one ormore biomarkers detected on the captured vesicle are PSMA and/or PCSA.In yet other embodiments, the capture agent targets CD63 and the one ormore biomarkers detected on the vesicle are CD81, CD83, CD9 and/or CD63.The different capture agent and biomarker combinations disclosed hereincan be used to characterize a phenotype, such as detecting, diagnosingor prognosing a disease, e.g., a cancer. In some embodiments, vesiclesare analyzed to characterize prostate cancer using a capture agenttargeting EpCam and detection of CD9 and CD63; a capture agent targetingPCSA and detection of B7H3 and PSMA; or a capture agent of CD63 anddetection of CD81. In other embodiments, vesicles are used tocharacterize colon cancer using capture agent targeting CD63 anddetection of CD63, or a capture agent targeting CD9 coupled withdetection of CD63. One of skill will appreciate that targets of captureagents and detection agents can be used interchangeably. In anillustrative example, consider a capture agent targeting PCSA anddetection agents targeting B7H3 and PSMA. Because all of these markersare useful for detecting PCa derived vesicles, B7H3 or PSMA could betargeted by the capture agent and PCSA could be recognized by adetection agent. For example, in some embodiments, the detection agenttargets PCSA, and one or more biomarkers used to capture the vesiclecomprise B7H3 and/or PSMA. In other embodiments, the detection agenttargets PSMA, and the one or more biomarkers used to capture the vesiclecomprise B7H3 and/or PCSA. In other embodiments, the detection agenttargets B7H3, and the one or more biomarkers used to capture the vesiclecomprise PSMA and/or PCSA. In some embodiments, the invention provides amethod of detecting prostate cancer cells in bodily fluid using captureagents and/or detection agents to PSMA, B7H3 and/or PCSA. The bodilyfluid can comprise blood, including serum or plasma. The bodily fluidcan comprise ejaculate or sperm. In further embodiments, the methods ofdetecting prostate cancer further use capture agents and/or detectionagents to CD81, CD83, CD9 and/or CD63. The method further provides amethod of characterizing a GI disorder, comprising capturing vesicleswith one or more of DR3, STEAP, epha2, TMEM211, unc93A, A33, CD24, NGAL,EpCam, MUC17, TROP2, and TETS, and detecting the captured vesicles withone or more general vesicle antigen, such as CD81, CD63 and/or CD9.Additional agents can improve the test performance, e.g., improving testaccuracy or AUC, either by providing additional biologicaldiscriminatory power and/or by reducing experimental noise.

Techniques of detecting biomarkers for use with the invention includethe use of a planar substrate such as an array (e.g., biochip ormicroarray), with molecules immobilized to the substrate as captureagents that facilitate the detection of a particular biosignature. Thearray can be provided as part of a kit for assaying one or morebiomarkers or vesicles. A molecule that identifies the biomarkersdescribed above and shown in FIGS. 3-60, as well as antigens in FIG. 1,can be included in an array for detection and diagnosis of diseasesincluding presymptomatic diseases. In some embodiments, an arraycomprises a custom array comprising biomolecules selected tospecifically identify biomarkers of interest. Customized arrays can bemodified to detect biomarkers that increase statistical performance,e.g., additional biomolecules that identifies a biosignature which leadto improved cross-validated error rates in multivariate predictionmodels (e.g., logistic regression, discriminant analysis, or regressiontree models). In some embodiments, customized array(s) are constructedto study the biology of a disease, condition or syndrome and profilebiosignatures in defined physiological states. Markers for inclusion onthe customized array be chosen based upon statistical criteria, e.g.,having a desired level of statistical significance in differentiatingbetween phenotypes or physiological states. In some embodiments,standard significance of p-value=0.05 is chosen to exclude or includebiomolecules on the microarray. The p-values can be corrected formultiple comparisons. As an illustrative example, nucleic acidsextracted from samples from a subject with or without a disease can behybridized to a high density microarray that binds to thousands of genesequences. Nucleic acids whose levels are significantly differentbetween the samples with or without the disease can be selected asbiomarkers to distinguish samples as having the disease or not. Acustomized array can be constructed to detect the selected biomarkers.In some embodiments, customized arrays comprise low density microarrays,which refer to arrays with lower number of addressable binding agents,e.g., tens or hundreds instead of thousands. Low density arrays can beformed on a substrate. In some embodiments, customizable low densityarrays use PCR amplification in plate wells, e.g., TaqMan® GeneExpression Assays (Applied Biosystems by Life Technologies Corporation,Carlsbad, Calif.).

A planar array generally contains addressable locations (e.g., pads,addresses, or micro-locations) of biomolecules in an array format. Thesize of the array will depend on the composition and end use of thearray. Arrays can be made containing from 2 different molecules to manythousands. Generally, the array comprises from two to as many as 100,000or more molecules, depending on the end use of the array and the methodof manufacture. A microarray for use with the invention comprises atleast one biomolecule that identifies or captures a biomarker present ina biosignature of interest, e.g., a microRNA or other biomolecule orvesicle that makes up the biosignature. In some arrays, multiplesubstrates are used, either of different or identical compositions.Accordingly, planar arrays may comprise a plurality of smallersubstrates.

The present invention can make use of many types of arrays for detectinga biomarker, e.g., a biomarker associated with a biosignature ofinterest. Useful arrays or microarrays include without limitation DNAmicroarrays, such as cDNA microarrays, oligonucleotide microarrays andSNP microarrays, microRNA arrays, protein microarrays, antibodymicroarrays, tissue microarrays, cellular microarrays (also calledtransfection microarrays), chemical compound microarrays, andcarbohydrate arrays (glycoarrays). These arrays are described in moredetail above. In some embodiments, microarrays comprise biochips thatprovide high-density immobilized arrays of recognition molecules (e.g.,antibodies), where biomarker binding is monitored indirectly (e.g., viafluorescence). FIG. 2A shows an illustrative configuration in whichcapture antibodies against a vesicle antigen of interest are tethered toa surface. The captured vesicles are then detected using detectorantibodies against the same or different vesicle antigens of interest.The capture antibodies can be substituted with tethered aptamers asavailable and desirable. Fluorescent detectors are shown. Otherdetectors can be used similarly, e.g., enzymatic reaction, detectablenanoparticles, radiolabels, and the like. In other embodiments, an arraycomprises a format that involves the capture of proteins by biochemicalor intermolecular interaction, coupled with detection by massspectrometry (MS). The vesicles can be eluted from the surface and thepayload therein, e.g., microRNA, can be analyzed.

An array or microarray that can be used to detect one or more biomarkersof a biosignature can be made according to the methods described in U.S.Pat. Nos. 6,329,209; 6,365,418; 6,406,921; 6,475,808; and 6,475,809, andU.S. patent application Ser. No. 10/884,269, each of which is hereinincorporated by reference in its entirety. Custom arrays to detectspecific selections of sets of biomarkers described herein can be madeusing the methods described in these patents. Commercially availablemicroarrays can also be used to carry out the methods of the invention,including without limitation those from Affymetrix (Santa Clara,Calif.), Illumina (San Diego, Calif.), Agilent (Santa Clara, Calif.),Exiqon (Denmark), or Invitrogen (Carlsbad, Calif.). Custom and/orcommercial arrays include arrays for detection proteins, nucleic acids,and other biological molecules and entities (e.g., cells, vesicles,virii) as described herein.

In some embodiments, molecules to be immobilized on an array compriseproteins or peptides. One or more types of proteins may be immobilizedon a surface. In certain embodiments, the proteins are immobilized usingmethods and materials that minimize the denaturing of the proteins, thatminimize alterations in the activity of the proteins, or that minimizeinteractions between the protein and the surface on which they areimmobilized.

Array surfaces useful may be of any desired shape, form, or size.Non-limiting examples of surfaces include chips, continuous surfaces,curved surfaces, flexible surfaces, films, plates, sheets, or tubes.Surfaces can have areas ranging from approximately a square micron toapproximately 500 cm². The area, length, and width of surfaces may bevaried according to the requirements of the assay to be performed.Considerations may include, for example, ease of handling, limitationsof the material(s) of which the surface is formed, requirements ofdetection systems, requirements of deposition systems (e.g., arrayers),or the like.

In certain embodiments, it is desirable to employ a physical means forseparating groups or arrays of binding islands or immobilizedbiomolecules: such physical separation facilitates exposure of differentgroups or arrays to different solutions of interest. Therefore, incertain embodiments, arrays are situated within microwell plates havingany number of wells. In such embodiments, the bottoms of the wells mayserve as surfaces for the formation of arrays, or arrays may be formedon other surfaces and then placed into wells. In certain embodiments,such as where a surface without wells is used, binding islands may beformed or molecules may be immobilized on a surface and a gasket havingholes spatially arranged so that they correspond to the islands orbiomolecules may be placed on the surface. Such a gasket is preferablyliquid tight. A gasket may be placed on a surface at any time during theprocess of making the array and may be removed if separation of groupsor arrays is no longer necessary.

In some embodiments, the immobilized molecules can bind to one or morebiomarkers or vesicles present in a biological sample contacting theimmobilized molecules. In some embodiments, the immobilized moleculesmodify or are modified by molecules present in the one or more vesiclescontacting the immobilized molecules. Contacting the sample typicallycomprises overlaying the sample upon the array.

Modifications or binding of molecules in solution or immobilized on anarray can be detected using detection techniques known in the art.Examples of such techniques include immunological techniques such ascompetitive binding assays and sandwich assays; fluorescence detectionusing instruments such as confocal scanners, confocal microscopes, orCCD-based systems and techniques such as fluorescence, fluorescencepolarization (FP), fluorescence resonant energy transfer (FRET), totalinternal reflection fluorescence (TIRF), fluorescence correlationspectroscopy (FCS); colorimetric/spectrometric techniques; surfaceplasmon resonance, by which changes in mass of materials adsorbed atsurfaces are measured; techniques using radioisotopes, includingconventional radioisotope binding and scintillation proximity assays(SPA); mass spectroscopy, such as matrix-assisted laserdesorption/ionization mass spectroscopy (MALDI) and MALDI-time of flight(TOF) mass spectroscopy; ellipsometry, which is an optical method ofmeasuring thickness of protein films; quartz crystal microbalance (QCM),a very sensitive method for measuring mass of materials adsorbing tosurfaces; scanning probe microscopies, such as atomic force microscopy(AFM), scanning force microscopy (SFM) or scanning electron microscopy(SEM); and techniques such as electrochemical, impedance, acoustic,microwave, and IR/Raman detection. See, e.g., Mere L, et al.,“Miniaturized FRET assays and microfluidics: key components forultra-high-throughput screening,” Drug Discovery Today 4(8):363-369(1999), and references cited therein; Lakowicz JR, Principles ofFluorescence Spectroscopy, 2nd Edition, Plenum Press (1999), or Jain KK: Integrative Omics, Pharmacoproteomics, and Human Body Fluids. In:Thongboonkerd V, ed., ed. Proteomics of Human Body Fluids: Principles,Methods and Applications. Volume 1: Totowa, N.J.: Humana Press, 2007,each of which is herein incorporated by reference in its entirety.

Microarray technology can be combined with mass spectroscopy (MS)analysis and other tools. Electrospray interface to a mass spectrometercan be integrated with a capillary in a microfluidics device. Forexample, one commercially available system contains eTag reporters thatare fluorescent labels with unique and well-defined electrophoreticmobilities; each label is coupled to biological or chemical probes viacleavable linkages. The distinct mobility address of each eTag reporterallows mixtures of these tags to be rapidly deconvoluted and quantitatedby capillary electrophoresis. This system allows concurrent geneexpression, protein expression, and protein function analyses from thesame sample Jain K K: Integrative Omics, Pharmacoproteomics, and HumanBody Fluids. In: Thongboonkerd V, ed., ed. Proteomics of Human BodyFluids: Principles, Methods and Applications. Volume 1: Totowa, N.J.:Humana Press, 2007, which is herein incorporated by reference in itsentirety.

A biochip can include components for a microfluidic or nanofluidicassay. A microfluidic device can be used for isolating or analyzingbiomarkers, such as determining a biosignature. Microfluidic systemsallow for the miniaturization and compartmentalization of one or moreprocesses for isolating, capturing or detecting a vesicle, detecting amicroRNA, detecting a circulating biomarker, detecting a biosignature,and other processes. The microfluidic devices can use one or moredetection reagents in at least one aspect of the system, and such adetection reagent can be used to detect one or more biomarkers. In oneembodiment, the device detects a biomarker on an isolated or boundvesicle. Various probes, antibodies, proteins, or other binding agentscan be used to detect a biomarker within the microfluidic system. Thedetection agents may be immobilized in different compartments of themicrofluidic device or be entered into a hybridization or detectionreaction through various channels of the device.

A vesicle in a microfluidic device can be lysed and its contentsdetected within the microfluidic device, such as proteins or nucleicacids, e.g., DNA or RNA such as miRNA or mRNA. The nucleic acid may beamplified prior to detection, or directly detected, within themicrofluidic device. Thus microfluidic system can also be used formultiplexing detection of various biomarkers. In an embodiment, vesiclesare captured within the microfluidic device, the captured vesicles arelysed, and a biosignature of microRNA from the vesicle payload isdetermined. The biosignature can further comprise the capture agent usedto capture the vesicle.

Novel nanofabrication techniques are opening up the possibilities forbiosensing applications that rely on fabrication of high-density,precision arrays, e.g., nucleotide-based chips and protein arraysotherwise know as heterogeneous nanoarrays. Nanofluidics allows afurther reduction in the quantity of fluid analyte in a microchip tonanoliter levels, and the chips used here are referred to as nanochips.(See, e.g., Unger M et al., Biotechniques 1999; 27(5):1008-14, KartalovE P et al., Biotechniques 2006; 40(1):85-90, each of which are hereinincorporated by reference in their entireties.) Commercially availablenanochips currently provide simple one step assays such as totalcholesterol, total protein or glucose assays that can be run bycombining sample and reagents, mixing and monitoring of the reaction.Gel-free analytical approaches based on liquid chromatography (LC) andnanoLC separations (Cutillas et al. Proteomics, 2005; 5:101-112 andCutillas et al., Mol Cell Proteomics 2005; 4:1038-1051, each of which isherein incorporated by reference in its entirety) can be used incombination with the nanochips.

Further provided herein is a rapid detection device that facilitates thedetection of a particular biosignature in a biological sample. Thedevice can integrate biological sample preparation with polymerase chainreaction (PCR) on a chip. The device can facilitate the detection of aparticular biosignature of a vesicle in a biological sample, and anexample is provided as described in Pipper et al., Angewandte Chemie,47(21), p. 3900-3904 (2008), which is herein incorporated by referencein its entirety. A biosignature can be incorporated usingmicro-/nano-electrochemical system (MEMS/NEMS) sensors and oral fluidfor diagnostic applications as described in Li et al., Adv Dent Res18(1): 3-5 (2005), which is herein incorporated by reference in itsentirety.

As an alternative to planar arrays, assays using particles, such as beadbased assays as described herein, can be used in combination with flowcytometry. Multiparametric assays or other high throughput detectionassays using bead coatings with cognate ligands and reporter moleculeswith specific activities consistent with high sensitivity automation canbe used. In a bead based assay system, a binding agent for a biomarkeror vesicle, such as a capture agent (e.g. capture antibody), can beimmobilized on an addressable microsphere. Each binding agent for eachindividual binding assay can be coupled to a distinct type ofmicrosphere (i.e., microbead) and the assay reaction takes place on thesurface of the microsphere, such as depicted in FIG. 63B. A bindingagent for a vesicle can be a capture antibody coupled to a bead. Dyedmicrospheres with discrete fluorescence intensities are loadedseparately with their appropriate binding agent or capture probes. Thedifferent bead sets carrying different binding agents can be pooled asnecessary to generate custom bead arrays. Bead arrays are then incubatedwith the sample in a single reaction vessel to perform the assay.Examples of microfluidic devices that may be used, or adapted for usewith the invention, include but are not limited to those describedherein.

Product formation of the biomarker with an immobilized capture moleculeor binding agent can be detected with a fluorescence based reportersystem (see for example, FIG. 63A-B). The biomarker can either belabeled directly by a fluorophore or detected by a second fluorescentlylabeled capture biomolecule. The signal intensities derived fromcaptured biomarkers can be measured in a flow cytometer. The flowcytometer can first identify each microsphere by its individual colorcode. For example, distinct beads can be dyed with discrete fluorescenceintensities such that each bead with a different intensity has adifferent binding agent. The beads can be labeled or dyed with at least2 different labels or dyes. In some embodiments, the beads are labeledwith at least 3, 4, 5, 6, 7, 8, 9, or 10 different labels. The beadswith more than one label or dye can also have various ratios andcombinations of the labels or dyes. The beads can be labeled or dyedexternally or may have intrinsic fluorescence or signaling labels.

The amount of captured biomarkers on each individual bead can bemeasured by the second color fluorescence specific for the bound target.This allows multiplexed quantitation of multiple targets from a singlesample within the same experiment. Sensitivity, reliability and accuracyare compared or can be improved to standard microtiter ELISA procedures.An advantage of a bead-based system is the individual coupling of thecapture biomolecule or binding agent for a vesicle to distinctmicrospheres provides multiplexing capabilities. For example, asdepicted in FIG. 63C, a combination of 5 different biomarkers to bedetected (detected by antibodies to antigens such as CD63, CD9, CD81,B7H3, and EpCam) and 20 biomarkers for which to capture a vesicle,(using capture antibodies, such as antibodies to CD9, PSCA, TNFR, CD63,B7H3, MFG-E8, EpCam, Rab, CD81, STEAP, PCSA, PSMA, 5T4, and/or CD24) canresult in approximately 100 combinations to be detected. As shown inFIG. 63C as “EpCam 2×,” “CD63 2×,” multiple antibodies to a singletarget can be used to probe detection against various epitopes. Inanother example, multiplex analysis comprises capturing a vesicle usinga binding agent to CD24 and detecting the captured vesicle using abinding agent for CD9, CD63, and/or CD81. The captured vesicles can bedetected using a detection agent such as an antibody. The detectionagents can be labeled directly or indirectly, as described herein.

Multiplexing of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,16, 17, 18, 19, 20, 25, 50, 75 or 100 different biomarkers may beperformed. For example, an assay of a heterogeneous population ofvesicles can be performed with a plurality of particles that aredifferentially labeled. There can be at least 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 50, 75 or 100differentially labeled particles. The particles may be externallylabeled, such as with a tag, or they may be intrinsically labeled. Eachdifferentially labeled particle can be coupled to a capture agent, suchas a binding agent, for a vesicle, resulting in capture of a vesicle.The multiple capture agents can be selected to characterize a phenotypeof interest, including capture agents against general vesiclebiomarkers, cell-of-origin specific biomarkers, and disease biomarkers.One or more biomarkers of the captured vesicle can then be detected by aplurality of binding agents. The binding agent can be directly labeledto facilitate detection. Alternatively, the binding agent is labeled bya secondary agent. For example, the binding agent may be an antibody fora biomarker on the vesicle. The binding agent is linked to biotin. Asecondary agent comprises streptavidin linked to a reporter and can beadded to detect the biomarker. In some embodiments, the captured vesicleis assayed for at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,16, 17, 18, 19, 20, 25, 50, 75 or 100 different biomarkers. For example,multiple detectors, i.e., detection of multiple biomarkers of a capturedvesicle or population of vesicles, can increase the signal obtained,permitted increased sensitivity, specificity, or both, and the use ofsmaller amounts of samples. For example, detection with more than onegeneral vesicle marker can improve the signal as compared to using alesser number of detection markers, such as a single marker. Toillustrate, detection of vesicles with labeled binding agents to two orthree of CD9, CD63 and CD81 can improve the signal compared to detectionwith any one of the tetraspanins individually.

An immunoassay based method or sandwich assay can also be used to detecta biomarker of a vesicle. An example includes ELISA. A binding agent orcapture agent can be bound to a well. For example an antibody to anantigen of a vesicle can be attached to a well. A biomarker on thecaptured vesicle can be detected based on the methods described herein.FIG. 63A shows an illustrative schematic for a sandwich-type ofimmunoassay. The capture antibody can be against a vesicle antigen ofinterest, e.g., a general vesicle biomarker, a cell-of-origin marker, ora disease marker. In the figure, the captured vesicles are detectedusing fluorescently labeled antibodies against vesicle antigens ofinterest. Multiple capture antibodies can be used, e.g., indistinguishable addresses on an array or different wells of animmunoassay plate. The detection antibodies can be against the sameantigen as the capture antibody, or can be directed against othermarkers. The capture antibodies can be substituted with alternatebinding agents, such as tethered aptamers or lectins, and/or thedetector antibodies can be similarly substituted, e.g., with detectable(e.g., labeled) aptamers, lectins or other binding proteins or entities.In an embodiment, one or more capture agents to a general vesiclebiomarker, a cell-of-origin marker, and/or a disease marker are usedalong with detection agents against general vesicle biomarker, such astetraspanin molecules including without limitation one or more of CD9,CD63 and CD81.

FIG. 63D presents an illustrative schematic for analyzing vesiclesaccording to the methods of the invention. Capture agents are used tocapture vesicles, detectors are used to detect the captured vesicles,and the level or presence of the captured and detected antibodies isused to characterize a phenotype. Capture agents, detectors andcharacterizing phenotypes can be any of those described herein. Forexample, capture agents include antibodies or aptamers tethered to asubstrate that recognize a vesicle antigen of interest, detectorsinclude labeled antibodies or aptamers to a vesicle antigen of interest,and characterizing a phenotype includes a diagnosis, prognosis, ortheranosis of a disease. In the scheme shown in FIG. 63D i), apopulation of vesicles is captured with one or more capture agentsagainst general vesicle biomarkers (6300). The captured vesicles arethen labeled with detectors against cell-of-origin biomarkers (6301)and/or disease specific biomarkers (6302). If only cell-of-origindetectors are used (6301), the biosignature used to characterize thephenotype (6303) can include the general vesicle markers (6300) and thecell-of-origin biomarkers (6301). If only disease detectors are used(6302), the biosignature used to characterize the phenotype (6303) caninclude the general vesicle markers (6300) and the disease biomarkers(6302). Alternately, detectors are used to detect both cell-of-originbiomarkers (6301) and disease specific biomarkers (6302). In this case,the biosignature used to characterize the phenotype (6303) can includethe general vesicle markers (6300), the cell-of-origin biomarkers (6301)and the disease biomarkers (6302). The biomarkers combinations areselected to characterize the phenotype of interest and can be selectedfrom the biomarkers and phenotypes described herein.

In the scheme shown in FIG. 63D ii), a population of vesicles iscaptured with one or more capture agents against cell-of-originbiomarkers (6310) and/or disease biomarkers (6311). The capturedvesicles are then detected using detectors against general vesiclebiomarkers (6312). If only cell-of-origin capture agents are used(6310), the biosignature used to characterize the phenotype (6313) caninclude the cell-of-origin biomarkers (6310) and the general vesiclemarkers (6312). If only disease biomarker capture agents are used(6311), the biosignature used to characterize the phenotype (6313) caninclude the disease biomarkers (6311) and the general vesicle biomarkers(6312). Alternately, capture agents to one or more cell-of-originbiomarkers (6310) and one or more disease specific biomarkers (6311) areused to capture vesicles. In this case, the biosignature used tocharacterize the phenotype (6313) can include the cell-of-originbiomarkers (6310), the disease biomarkers (6311), and the generalvesicle markers (6313). The biomarkers combinations are selected tocharacterize the phenotype of interest and can be selected from thebiomarkers and phenotypes described herein.

Biomarkers comprising vesicle payload can be analyzed to characterize aphenotype. Payload comprises the biological entities contained within avesicle membrane. These entities include without limitation nucleicacids, e.g., mRNA, microRNA, or DNA fragments; protein, e.g., solubleand membrane associated proteins; carbohydrates; lipids; metabolites;and various small molecules, e.g., hormones. The payload can be part ofthe cellular milieu that is encapsulated as a vesicle is formed in thecellular environment. In some embodiments of the invention, the payloadis analyzed in addition to detecting vesicle surface antigens. Specificpopulations of vesicles can be captured as described above then thepayload in the captured vesicles can be used to characterize aphenotype. For example, vesicles captured on a substrate can be furtherisolated to assess the payload therein. Alternately, the vesicles in asample are detected and sorted without capture. The vesicles so detectedcan be further isolated to assess the payload therein. In an embodiment,vesicle populations are sorted by flow cytometry and the payload in thesorted vesicles is analyzed. In the scheme shown in FIG. 63E iii), apopulation of vesicles is captured and/or detected (6320) using one ormore of cell-of-origin biomarkers (6320), disease biomarkers (6321), andgeneral vesicle markers (6322). The payload of the isolated vesicles isassessed (6323). A biosignature detected within the payload can be usedto characterize a phenotype (6324). In a non-limiting example, a vesiclepopulation can be analyzed in a plasma sample from a patient usingantibodies against one or more vesicle antigens of interest. Theantibodies can be capture antibodies which are tethered to a substrateto isolate a desired vesicle population. Alternately, the antibodies canbe directly labeled and the labeled vesicles isolated by sorting withflow cytometry. The presence or level of microRNA or mRNA extracted fromthe isolated vesicle population can be used to detect a biosignature.The biosignature is then used to diagnose, prognose or theranose thepatient.

In other embodiments, vesicle payload is analyzed in a vesiclepopulation without first capturing or detected subpopulations ofvesicles. For example, vesicles can be generally isolated from a sampleusing centrifugation, filtration, chromatography, or other techniques asdescribed herein. The payload of the isolated vesicles can be analyzedthereafter to detect a biosignature and characterize a phenotype. In thescheme shown in FIG. 63E iv), a population of vesicles is isolated(6330) and the payload of the isolated vesicles is assessed (6331). Abiosignature detected within the payload can be used to characterize aphenotype (6332). In a non-limiting example, a vesicle population isisolated from a plasma sample from a patient using size exclusion andmembrane filtration. The presence or level of microRNA or mRNA extractedfrom the vesicle population is used to detect a biosignature. Thebiosignature is then used to diagnose, prognose or theranose thepatient.

A peptide or protein biomarker can be analyzed by mass spectrometry orflow cytometry. Proteomic analysis of a vesicle may be carried out byimmunocytochemical staining, Western blotting, electrophoresis,SDS-PAGE, chromatography, x-ray crystallography or other proteinanalysis techniques in accordance with procedures well known in the art.In other embodiments, the protein biosignature of a vesicle may beanalyzed using 2 D differential gel electrophoresis as described in,Chromy et al. J Proteome Res, 2004; 3:1120-1127, which is hereinincorporated by reference in its entirety, or with liquid chromatographymass spectrometry as described in Zhang et al. Mol Cell Proteomics,2005; 4:144-155, which is herein incorporated by reference in itsentirety. A vesicle may be subjected to activity-based protein profilingdescribed for example, in Berger et al., Am J Pharmacogenomics, 2004;4:371-381, which is in incorporated by reference in its entirety. Inother embodiments, a vesicle may be profiled using nanospray liquidchromatography-tandem mass spectrometry as described in Pisitkun et al.,Proc Natl Acad Sci USA, 2004; 101:13368-13373, which is hereinincorporated by reference in its entirety. In another embodiment, thevesicle may be profiled using tandem mass spectrometry (MS) such asliquid chromatography/MS/MS (LC-MS/MS) using for example a LTQ andLTQ-FT ion trap mass spectrometer. Protein identification can bedetermined and relative quantitation can be assessed by comparingspectral counts as described in Smalley et al., J Proteome Res, 2008;7:2088-2096, which is herein incorporated by reference in its entirety.

The expression of circulating protein biomarkers or protein payloadwithin a vesicle can also be identified. The latter analysis canoptionally follow the isolation of specific vesicles using captureagents to capture populations of interest. In an embodiment,immunocytochemical staining is used to analyze protein expression. Thesample can be resuspended in buffer, centrifuged at 100×g for example,for 3 minutes using a cytocentrifuge on adhesive slides in preparationfor immunocytochemical staining. The cytospins can be air-driedovernight and stored at −80° C. until staining. Slides can then be fixedand blocked with serum-free blocking reagent. The slides can then beincubated with a specific antibody to detect the expression of a proteinof interest. In some embodiments, the vesicles are not purified,isolated or concentrated prior to protein expression analysis.

Biosignatures comprising vesicle payload can be characterized byanalysis of a metabolite marker or metabolite within the vesicle.Various metabolite-oriented approaches have been described such asmetabolite target analyses, metabolite profiling, or metabolicfingerprinting, see for example, Denkert et al., Molecular Cancer 2008;7: 4598-4617, Ellis et al., Analyst 2006; 8: 875-885, Kuhn et al.,Clinical Cancer Research 2007; 24: 7401-7406, Fiehn O., Comp FunctGenomics 2001; 2:155-168, Fancy et al., Rapid Commun Mass Spectrom20(15): 2271-80 (2006), Lindon et al., Pharm Res, 23(6): 1075-88 (2006),Holmes et al., Anal Chem. 2007 Apr. 1; 79(7):2629-40. Epub 2007 Feb. 27.Erratum in: Anal Chem. 2008 Aug. 1; 80(15):6142-3, Stanley et al., AnalBiochem. 2005 Aug. 15; 343(2): 195-202., Lehtimäki et al., J Biol Chem.2003 Nov. 14; 278(46):45915-23, each of which is herein incorporated byreference in its entirety.

Peptides can be analyzed by systems described in Jain K K: IntegrativeOmics, Pharmacoproteomics, and Human Body Fluids. In: Thongboonkerd V,ed., ed. Proteomics of Human Body Fluids: Principles, Methods andApplications. Volume 1: Totowa, N.J.: Humana Press, 2007, which isherein incorporated by reference in its entirety. This system cangenerate sensitive molecular fingerprints of proteins present in a bodyfluid as well as in vesicles. Commercial applications which include theuse of chromatography/mass spectroscopy and reference libraries of allstable metabolites in the human body, for example Paradigm Genetic'sHuman Metabolome Project, may be used to determine a metabolitebiosignature. Other methods for analyzing a metabolic profile caninclude methods and devices described in U.S. Pat. No. 6,683,455(Metabometrix), U.S. Patent Application Publication Nos. 20070003965 and20070004044 (Biocrates Life Science), each of which is hereinincorporated by reference in its entirety. Other proteomic profilingtechniques are described in Kennedy, Toxicol Lett 120:379-384 (2001),Berven et al., Curr Pharm Biotechnol 7(3): 147-58 (2006), Conrads etal., Expert Rev Proteomics 2(5): 693-703, Decramer et al., World J Urol25(5): 457-65 (2007), Decramer et al., Mol Cell Proteomics 7(10):1850-62 (2008), Decramer et al., Contrib Nephrol, 160: 127-41 (2008),Diamandis, J Proteome Res 5(9): 2079-82 (2006), Immler et al.,Proteomics 6(10): 2947-58 (2006), Khan et al., J Proteome Res 5(10):2824-38 (2006), Kumar et al., Biomarkers 11(5): 385-405 (2006), Noble etal., Breast Cancer Res Treat 104(2): 191-6 (2007), Omenn, Dis Markers20(3): 131-4 (2004), Powell et al., Expert Rev Proteomics 3(1): 63-74(2006), Rai et al., Arch Pathol Lab Med, 126(12): 1518-26 (2002),Ramstrom et al., Proteomics, 3(2): 184-90 (2003), Tammen et al., BreastCancer Res Treat, 79(1): 83-93 (2003), Theodorescu et al., Lancet Oncol,7(3): 230-40 (2006), or Zurbig et al., Electrophoresis, 27(11): 2111-25(2006).

For analysis of mRNAs, miRNAs or other small RNAs, the total RNA can beisolated using any known methods for isolating nucleic acids such asmethods described in U.S. Patent Application Publication No. 2008132694,which is herein incorporated by reference in its entirety. Theseinclude, but are not limited to, kits for performing membrane based RNApurification, which are commercially available. Generally, kits areavailable for the small-scale (30 mg or less) preparation of RNA fromcells and tissues, for the medium scale (250 mg tissue) preparation ofRNA from cells and tissues, and for the large scale (1 g maximum)preparation of RNA from cells and tissues. Other commercially availablekits for effective isolation of small RNA-containing total RNA areavailable. Such methods can be used to isolate nucleic acids fromvesicles.

Alternatively, RNA can be isolated using the method described in U.S.Pat. No. 7,267,950, which is herein incorporated by reference in itsentirety. U.S. Pat. No. 7,267,950 describes a method of extracting RNAfrom biological systems (cells, cell fragments, organelles, tissues,organs, or organisms) in which a solution containing RNA is contactedwith a substrate to which RNA can bind and RNA is withdrawn from thesubstrate by applying negative pressure. Alternatively, RNA may beisolated using the method described in U.S. Patent Application No.20050059024, which is herein incorporated by reference in its entirety,which describes the isolation of small RNA molecules. Other methods aredescribed in U.S. Patent Application No. 20050208510, 20050277121,20070238118, each of which is incorporated by reference in its entirety.

In one embodiment, mRNA expression analysis can be carried out on mRNAsfrom a vesicle isolated from a sample. In some embodiments, the vesicleis a cell-of-origin specific vesicle. An expression pattern generatedfrom a vesicle can be indicative of a given disease state, diseasestage, therapy related signature, or physiological condition.

In one embodiment, once the total RNA has been isolated, cDNA can besynthesized and either qRT-PCR assays (e.g. Applied Biosystem's Taqman®assays) for specific mRNA targets can be performed according tomanufacturer's protocol, or an expression microarray can be performed tolook at highly multiplexed sets of expression markers in one experiment.Methods for establishing gene expression profiles include determiningthe amount of RNA that is produced by a gene that can code for a proteinor peptide. This can be accomplished by quantitative reversetranscriptase PCR (qRT-PCR), competitive RT-PCR, real time RT-PCR,differential display RT-PCR, Northern Blot analysis or other relatedtests. While it is possible to conduct these techniques using individualPCR reactions, it is also possible to amplify complementary DNA (cDNA)or complementary RNA (cRNA) produced from mRNA and analyze it viamicroarray.

The level of a miRNA product in a sample can be measured using anyappropriate technique that is suitable for detecting mRNA expressionlevels in a biological sample, including but not limited to Northernblot analysis, RT-PCR, qRT-PCR, in situ hybridization or microarrayanalysis. For example, using gene specific primers and target cDNA,qRT-PCR enables sensitive and quantitative miRNA measurements of eithera small number of target miRNAs (via singleplex and multiplex analysis)or the platform can be adopted to conduct high throughput measurementsusing 96-well or 384-well plate formats. See for example, Ross J S etal, Oncologist. 2008 May; 13(5):477-93, which is herein incorporated byreference in its entirety. A number of different array configurationsand methods for microarray production are known to those of skill in theart and are described in U.S. patents such as: U.S. Pat. Nos. 5,445,934;5,532,128; 5,556,752; 5,242,974; 5,384,261; 5,405,783; 5,412,087;5,424,186; 5,429,807; 5,436,327; 5,472,672; 5,527,681; 5,529,756;5,545,531; 5,554,501; 5,561,071; 5,571,639; 5,593,839; 5,599,695;5,624,711; 5,658,734; or 5,700,637; each of which is herein incorporatedby reference in its entirety. Other methods of profiling miRNAs aredescribed in Taylor et al., Gynecol Oncol. 2008 July; 110(1): 13-21,Gilad et al, PLoS ONE. 2008 Sep. 5; 3(9):e3148, Lee et al., Annu RevPathol. 2008 Sep. 25 and Mitchell et al, Proc Natl Acad Sci USA. 2008Jul. 29; 105(30):10513-8, Shen R et al, BMC Genomics. 2004 Dec. 14;5(1):94, Mina L et al, Breast Cancer Res Treat. 2007 June;103(2):197-208, Zhang L et al, Proc Natl Acad Sci USA. 2008 May 13;105(19):7004-9, Ross J S et al, Oncologist. 2008 May; 13(5):477-93,Schetter A J et al, JAMA. 2008 Jan. 30; 299(4):425-36, Staudt L M, NEngl J Med 2003; 348:1777-85, Mulligan G et al, Blood. 2007 Apr. 15;109(8):3177-88. Epub 2006 Dec. 21, McLendon R et al, Nature. 2008 Oct.23; 455(7216):1061-8, and U.S. Pat. Nos. 5,538,848, 5,723,591,5,876,930, 6,030,787, 6,258,569, and 5,804,375, each of which is hereinincorporated by reference. In some embodiments, arrays of microRNApanels are use to simultaneously query the expression of multiple miRs.The Exiqon mIRCURY LNA microRNA PCR system panel (Exiqon, Inc., Woburn,Mass.) or the TaqMan® MicroRNA Assays and Arrays systems from AppliedBiosystems (Foster City, Calif.) can be used for such purposes.

Microarray technology allows for the measurement of the steady-statemRNA or miRNA levels of thousands of transcripts or miRNAssimultaneously thereby presenting a powerful tool for identifyingeffects such as the onset, arrest, or modulation of uncontrolled cellproliferation. Two microarray technologies, such as cDNA arrays andoligonucleotide arrays can be used. The product of these analyses aretypically measurements of the intensity of the signal received from alabeled probe used to detect a cDNA sequence from the sample thathybridizes to a nucleic acid sequence at a known location on themicroarray. Typically, the intensity of the signal is proportional tothe quantity of cDNA, and thus mRNA or miRNA, expressed in the samplecells. A large number of such techniques are available and useful.Methods for determining gene expression can be found in U.S. Pat. No.6,271,002 to Linsley, et al.; U.S. Pat. No. 6,218,122 to Friend, et al.;U.S. Pat. No. 6,218,114 to Peck et al.; or U.S. Pat. No. 6,004,755 toWang, et al., each of which is herein incorporated by reference in itsentirety.

Analysis of an expression level can be conducted by comparing suchintensities. This can be performed by generating a ratio matrix of theexpression intensities of genes in a test sample versus those in acontrol sample. The control sample may be used as a reference, anddifferent references to account for age, ethnicity and sex may be used.Different references can be used for different conditions or diseases,as well as different stages of diseases or conditions, as well as fordetermining therapeutic efficacy.

For instance, the gene expression intensities of mRNA or miRNAs derivedfrom a diseased tissue, including those isolated from vesicles, can becompared with the expression intensities of the same entities in normaltissue of the same type (e.g., diseased breast tissue sample versusnormal breast tissue sample). A ratio of these expression intensitiesindicates the fold-change in gene expression between the test andcontrol samples. Alternatively, if vesicles are not normally present infrom normal tissues (e.g. breast) then absolute quantitation methods, asis known in the art, can be used to define the number of miRNA moleculespresent without the requirement of miRNA or mRNA isolated from vesiclesderived from normal tissue.

Gene expression profiles can also be displayed in a number of ways. Acommon method is to arrange raw fluorescence intensities or ratio matrixinto a graphical dendogram where columns indicate test samples and rowsindicate genes. The data is arranged so genes that have similarexpression profiles are proximal to each other. The expression ratio foreach gene is visualized as a color. For example, a ratio less than one(indicating down-regulation) may appear in the blue portion of thespectrum while a ratio greater than one (indicating upregulation) mayappear as a color in the red portion of the spectrum. Commerciallyavailable computer software programs are available to display such data.

mRNAs or miRNAs that are considered differentially expressed can beeither over expressed or under expressed in patients with a diseaserelative to disease free individuals. Over and under expression arerelative terms meaning that a detectable difference (beyond thecontribution of noise in the system used to measure it) is found in theamount of expression of the mRNAs or miRNAs relative to some baseline.In this case, the baseline is the measured mRNA/miRNA expression of anon-diseased individual. The mRNA/miRNA of interest in the diseasedcells can then be either over or under expressed relative to thebaseline level using the same measurement method. Diseased, in thiscontext, refers to an alteration of the state of a body that interruptsor disturbs, or has the potential to disturb, proper performance ofbodily functions as occurs with the uncontrolled proliferation of cells.Someone is diagnosed with a disease when some aspect of that person'sgenotype or phenotype is consistent with the presence of the disease.However, the act of conducting a diagnosis or prognosis includes thedetermination of disease/status issues such as determining thelikelihood of relapse or metastasis and therapy monitoring. In therapymonitoring, clinical judgments are made regarding the effect of a givencourse of therapy by comparing the expression of genes over time todetermine whether the mRNA/miRNA expression profiles have changed or arechanging to patterns more consistent with normal tissue.

Levels of over and under expression are distinguished based on foldchanges of the intensity measurements of hybridized microarray probes. A2× difference is preferred for making such distinctions or a p-valueless than 0.05. That is, before an mRNA/miRNA is the to bedifferentially expressed in diseased/relapsing versusnormal/non-relapsing cells, the diseased cell is found to yield at least2 times more, or 2 times less intensity than the normal cells. Thegreater the fold difference, the more preferred is use of the gene as adiagnostic or prognostic tool. mRNA/miRNAs selected for the expressionprofiles of the instant invention have expression levels that result inthe generation of a signal that is distinguishable from those of thenormal or non-modulated genes by an amount that exceeds background usingclinical laboratory instrumentation.

Statistical values can be used to confidently distinguish modulated fromnon-modulated mRNA/miRNA and noise. Statistical tests find themRNA/miRNA most significantly different between diverse groups ofsamples. The Student's t-test is an example of a robust statistical testthat can be used to find significant differences between two groups. Thelower the p-value, the more compelling the evidence that the gene showsa difference between the different groups. Nevertheless, sincemicroarrays measure more than one mRNA/miRNA at a time, tens ofthousands of statistical tests may be performed at one time. Because ofthis, one is unlikely to see small p-values just by chance andadjustments for this using a Sidak correction as well as arandomization/permutation experiment can be made. A p-value less than0.05 by the t-test is evidence that the gene is significantly different.More compelling evidence is a p-value less then 0.05 after the Sidakcorrection is factored in. For a large number of samples in each group,a p-value less than 0.05 after the randomization/permutation test is themost compelling evidence of a significant difference.

In one embodiment, a method of generating a posterior probability scoreto enable diagnostic, prognostic, therapy-related, or physiologicalstate specific biosignature scores can be arrived at by obtaining mRNAor miRNA (biomarker) expression data from a statistically significantnumber of patients; applying linear discrimination analysis to the datato obtain selected biomarkers; and applying weighted expression levelsto the selected biomarkers with discriminate function factor to obtain aprediction model that can be applied as a posterior probability score.Other analytical tools can also be used to answer the same question suchas, logistic regression and neural network approaches.

For instance, the following can be used for linear discriminantanalysis:

where,

-   -   I(p_(s)i_(d))=The log base 2 intensity of the probe set enclosed        in parenthesis. d(cp)=The discriminant function for the disease        positive class d(C_(N))=The discriminant function for the        disease negative class    -   P(_(CP))=The posterior p-value for the disease positive class    -   P(_(CN))=The posterior p-value for the disease negative class

Numerous other well-known methods of pattern recognition are available.The following references provide some examples: Weighted Voting: Golubet al. (1999); Support Vector Machines: Su et al. (2001); and Ramaswamyet al. (2001); K-nearest Neighbors: Ramaswamy (2001); and CorrelationCoefficients: van 't Veer et al. (2002), all of which are hereinincorporated by reference in their entireties.

A biosignature portfolio, further described below, can be establishedsuch that the combination of biomarkers in the portfolio exhibitimproved sensitivity and specificity relative to individual biomarkersor randomly selected combinations of biomarkers. In one embodiment, thesensitivity of the biosignature portfolio can be reflected in the folddifferences, for example, exhibited by a transcript's expression in thediseased state relative to the normal state. Specificity can bereflected in statistical measurements of the correlation of thesignaling of transcript expression with the condition of interest. Forexample, standard deviation can be a used as such a measurement. Inconsidering a group of biomarkers for inclusion in a biosignatureportfolio, a small standard deviation in expression measurementscorrelates with greater specificity. Other measurements of variationsuch as correlation coefficients can also be used in this capacity.

Another parameter that can be used to select mRNA/miRNA that generate asignal that is greater than that of the non-modulated mRNA/miRNA ornoise is the use of a measurement of absolute signal difference. Thesignal generated by the modulated mRNA/miRNA expression is at least 20%different than those of the normal or non-modulated gene (on an absolutebasis). It is even more preferred that such mRNA/miRNA produceexpression patterns that are at least 30% different than those of normalor non-modulated mRNA/miRNA.

MiRNA can also be detected and measured by amplification from abiological sample and measured using methods described in U.S. Pat. No.7,250,496, U.S. Application Publication Nos. 20070292878, 20070042380 or20050222399 and references cited therein, each of which is hereinincorporated by reference in its entirety. The microRNA can be assessedas in U.S. Pat. No. 7,888,035, entitled “METHODS FOR ASSESSING RNAPATTERNS,” issued Feb. 15, 2011, which application is incorporated byreference herein in its entirety.

Peptide nucleic acids (PNAs) which are a new class of synthetic nucleicacid analogs in which the phosphate-sugar polynucleotide backbone isreplaced by a flexible pseudo-peptide polymer may be utilized inanalysis of a biosignature. PNAs are capable of hybridizing with highaffinity and specificity to complementary RNA and DNA sequences and arehighly resistant to degradation by nucleases and proteinases. Peptidenucleic acids (PNAs) are an attractive new class of probes withapplications in cytogenetics for the rapid in situ identification ofhuman chromosomes and the detection of copy number variation (CNV).Multicolor peptide nucleic acid-fluorescence in situ hybridization(PNA-FISH) protocols have been described for the identification ofseveral human CNV-related disorders and infectious diseases. PNAs canalso be utilized as molecular diagnostic tools to non-invasively measureoncogene mRNAs with tumor targeted radionuclide-PNA-peptide chimeras.Methods of using PNAs are described further in Pellestor F et al, CurrPharm Des. 2008; 14(24):2439-44, Tian X et al, Ann N Y Acad Sci. 2005November; 1059:106-44, Paulasova P and Pellestor F, Annales deGénétique, 47 (2004) 349-358, Stender H. Expert Rev Mol Diagn. 2003September; 3(5):649-55. Review, Vigneault et al., Nature Methods, 5(9),777-779 (2008), each reference is herein incorporated by reference inits entirety. These methods can be used to screen the genetic materialsisolated from a vesicle. When applying these techniques to acell-of-origin specific vesicle, they can be used to identify a givenmolecular signal that directly pertains to the cell of origin.

Mutational analysis may be carried out for mRNAs and DNA, includingthose that are identified from a vesicle. For mutational analysis of atarget or biomarker that is of RNA origin, the RNA (mRNA, miRNA orother) can be reverse transcribed into cDNA and subsequently sequencedor assayed, such as for known SNPs (by Taqman SNP assays, for example)or single nucleotide mutations, as well as using sequencing to look forinsertions or deletions to determine mutations present in thecell-of-origin. Multiplexed ligation dependent probe amplification(MLPA) could alternatively be used for the purpose of identifying CNV insmall and specific areas of interest. For example, once the total RNAhas been obtained from isolated colon cancer-specific vesicles, cDNA canbe synthesized and primers specific for exons 2 and 3 of the KRAS genecan be used to amplify these two exons containing codons 12, 13 and 61of the KRAS gene. The same primers used for PCR amplification can beused for Big Dye Terminator sequence analysis on the ABI 3730 toidentify mutations in exons 2 and 3 of KRAS. Mutations in these codonsare known to confer resistance to drugs such as Cetuximab andPanitumimab. Methods of conducting mutational analysis are described inMaheswaran S et al, Jul. 2, 2008 (10.1056/NEJMoa0800668) and Orita, M etal, PNAS 1989, (86): 2766-70, each of which is herein incorporated byreference in its entirety.

Other methods of conducting mutational analysis include miRNAsequencing. Applications for identifying and profiling miRNAs can bedone by cloning techniques and the use of capillary DNA sequencing or“next-generation” sequencing technologies. The new sequencingtechnologies currently available allow the identification oflow-abundance miRNAs or those exhibiting modest expression differencesbetween samples, which may not be detected by hybridization-basedmethods. Such new sequencing technologies include the massively parallelsignature sequencing (MPSS) methodology described in Nakano et al. 2006,Nucleic Acids Res. 2006; 34:D731-D735. doi: 10.1093/nar/gkj077, theRoche/454 platform described in Margulies et al. 2005, Nature. 2005;437:376-380 or the Illumina sequencing platform described in Berezikovet al. Nat. Genet. 2006b; 38:1375-1377, each of which is incorporated byreference in its entirety.

Additional methods to determine a biosignature includes assaying abiomarker by allele-specific PCR, which includes specific primers toamplify and discriminate between two alleles of a gene simultaneously,single-strand conformation polymorphism (SSCP), which involves theelectrophoretic separation of single-stranded nucleic acids based onsubtle differences in sequence, and DNA and RNA aptamers. DNA and RNAaptamers are short oligonucleotide sequences that can be selected fromrandom pools based on their ability to bind a particular molecule withhigh affinity. Methods of using aptamers are described in Ulrich H etal, Comb Chem High Throughput Screen. 2006 September; 9(8):619-32,Ferreira C S et al, Anal Bioanal Chem. 2008 February; 390(4):1039-50,Ferreira C S et al, Tumour Biol. 2006; 27(6):289-301, each of which isherein incorporated by reference in its entirety.

Biomarkers can also be detected using fluorescence in situ hybridization(FISH). Methods of using FISH to detect and localize specific DNAsequences, localize specific mRNAs within tissue samples or identifychromosomal abnormalities are described in Shaffer D R et al, ClinCancer Res. 2007 Apr. 1; 13(7):2023-9, Cappuzo F et al, Journal ofThoracic Oncology, Volume 2, Number 5, May 2007, Moroni M et al, LancetOncol. 2005 May; 6(5):279-86, each of which is herein incorporated byreference in its entirety.

An illustrative schematic for analyzing a population of vesicles fortheir payload is presented in FIG. 63E. In an embodiment, the methods ofthe invention include characterizing a phenotype by capturing vesicles(6330) and determining a level of microRNA species contained therein(6331), thereby characterizing the phenotype (6332).

A biosignature comprising a circulating biomarker or vesicle cancomprise a binding agent thereto. The binding agent can be a DNA, RNA,aptamer, monoclonal antibody, polyclonal antibody, Fabs, Fab′, singlechain antibody, synthetic antibody, aptamer (DNA/RNA), peptoid, zDNA,peptide nucleic acid (PNA), locked nucleic acid (LNA), lectin, syntheticor naturally occurring chemical compounds (including but not limited todrugs and labeling reagents).

A binding agent can used to isolate or detect a vesicle by binding to acomponent of the vesicle, as described above. The binding agent can beused to detect a vesicle, such as for detecting a cell-of-originspecific vesicle. A binding agent or multiple binding agents canthemselves form a binding agent profile that provides a biosignature fora vesicle. One or more binding agents can be selected from FIG. 2. Forexample, if a vesicle population is detected or isolated using two,three or four binding agents in a differential detection or isolation ofa vesicle from a heterogeneous population of vesicles, the particularbinding agent profile for the vesicle population provides a biosignaturefor the particular vesicle population.

As an illustrative example, a vesicle for characterizing a cancer can bedetected with one or more binding agents including, but not limited to,PSA, PSMA, PCSA, PSCA, B7H3, EpCam, TMPRSS2, mAB 5D4, XPSM-A9, XPSM-A10,Galectin-3, E-selectin, Galectin-1, or E4 (IgG2a kappa), or anycombination thereof.

The binding agent can also be for a general vesicle biomarker, such as a“housekeeping protein” or antigen. The biomarker can be CD9, CD63, orCD81. For example, the binding agent can be an antibody for CD9, CD63,or CD81. The binding agent can also be for other proteins, such as fortissue specific or cancer specific vesicles. The binding agent can befor PCSA, PSMA, EpCam, B7H3, or STEAP. The binding agent can be for DR3,STEAP, epha2, TMEM211, MFG-E8, Annexin V, TF, unc93A, A33, CD24, NGAL,EpCam, MUC17, TROP2, or TETS. For example, the binding agent can be anantibody or aptamer for PCSA, PSMA, EpCam, B7H3, DR3, STEAP, epha2,TMEM211, MFG-E8, Annexin V, TF, unc93A, A33, CD24, NGAL, EpCam, MUC17,TROP2, or TETS.

Various proteins are not typically distributed evenly or uniformly on avesicle shell. See, e.g., FIG. 64, which illustrates a schematic ofprotein expression patterns. Vesicle-specific proteins are typicallymore common, while cancer-specific proteins are less common. In someembodiments, capture of a vesicle is accomplished using a more common,less cancer-specific protein, such as one or more housekeeping proteinsor antigen or general vesicle antigen (e.g., a tetraspanin), and one ormore cancer-specific biomarkers and/or one or more cell-of-originspecific biomarkers is used in the detection phase. In anotherembodiment, one or more cancer-specific biomarkers and/or one or morecell-of-origin specific biomarkers are used for capture, and one or morehousekeeping proteins or antigen or general vesicle antigen (e.g., atetraspanin) is used for detection. In embodiments, the same biomarkeris used for both capture and detection. Different binding agents for thesame biomarker can be used, such as antibodies or aptamers that binddifferent epitopes of an antigen.

Additional cellular binding partners or binding agents may be identifiedby any conventional methods known in the art, or as described herein,and may additionally be used as a diagnostic, prognostic ortherapy-related marker.

As an illustrative example, a vesicle for analysis for lung cancer canbe detected with one or more binding agents including, but not limitedto, SCLC specific aptamer HCA 12, SCLC specific aptamer HCC03, SCLCspecific aptamer HCH07, SCLC specific aptamer HCH01, A-p50 aptamer(NF-KB), Cetuximab, Panitumumab, Bevacizumab, L19 Ab, F16 Ab, anti-CD45(anti-ICAM-1, aka UV3), or L2G7 Ab (anti-HGF), or any combinationthereof.

A vesicle for characterizing colon cancer can be detected with one ormore binding agents including, but not limited to, angiopoietin 2specific aptamer, beta-catenin aptamer, TCF1 aptamer, anti-Derlin1 ab,anti-RAGE, mAbgb3.1, Galectin-3, Cetuximab, Panitumumab, Matuzumab,Bevacizumab, or Mac-2, or any combination thereof.

A vesicle for characterizing adenoma versus colorectal cancer (CRC) canbe detected with one or more binding agents including, but not limitedto, Complement C3, histidine-rich glycoprotein, kininogen-1, orGalectin-3, or any combination thereof.

A vesicle for characterizing adenoma with low grade hyperplasia versusadenoma with high grade hyperplasia can be detected with a binding agentsuch as, but not limited to, Galectin-3 or any combination of bindingagents specific for this comparison.

A vesicle for characterizing CRC versus normal state can be detectedwith one or more binding agents including, but not limited to, anti-ODCmAb, anti-CEA mAb, or Mac-2, or any combination thereof.

A vesicle for characterizing prostate cancer can be detected with one ormore binding agents including, but not limited to, PSA, PSMA, TMPRSS2,mAB 5D4, XPSM-A9, XPSM-A10, Galectin-3, E-selectin, Galectin-1, or E4(IgG2a kappa), or any combination thereof.

A vesicle for characterizing melanoma can be detected with one or morebinding agents including, but not limited to, Tremelimumab (anti-CTLA4),Ipilimumumab (anti-CTLA4), CTLA-4 aptamers, STAT-3 peptide aptamers,Galectin-1, Galectin-3, or PNA, or any combination thereof.

A vesicle for characterizing pancreatic cancer can be detected with oneor more binding agents including, but not limited to, H38-15 (anti-HGF)aptamer, H38-21(anti-HGF) aptamer, Matuzumab, Cetuximanb, orBevacizumab, or any combination thereof.

A vesicle for characterizing brain cancer can be detected with one ormore binding agents including, but not limited to, aptamer III.1(pigpen) and/or TTA1 (Tenascin-C) aptamer, or any combination thereof.

A vesicle for characterizing psoriasis can be detected with one or morebinding agents including, but not limited to, E-selectin, ICAM-1, VLA-4,VCAM-1, alphaEbeta7, or any combination thereof.

A vesicle for characterizing cardiovascular disease (CVD) can bedetected with one or more binding agents including, but not limited to,RB007 (factor IXA aptamer), ARC1779 (anti VWF) aptamer, or LOX1, or anycombination thereof.

A vesicle for characterizing hematological malignancies can be detectedwith one or more binding agents including, but not limited to, anti-CD20and/or anti-CD52, or any combination thereof.

A vesicle for characterizing B-cell chronic lymphocytic leukemias can bedetected with one or more binding agents including, but not limited to,Rituximab, Alemtuzumab, Apt48 (BCL6), R0-60, or D-R15-8, or anycombination thereof.

A vesicle for characterizing B-cell lymphoma can be detected with one ormore binding agents including, but not limited to, Ibritumomab,Tositumomab, Anti-CD20 Antibodies, Alemtuzumab, Galiximab, Anti-CD40Antibodies, Epratuzumab, Lumiliximab, Hu1D10, Galectin-3, or Apt48, orany combination thereof.

A vesicle for characterizing Burkitt's lymphoma can be detected with oneor more binding agents including, but not limited to, TD05 aptamer, IgMmAB (38-13), or any combination thereof.

A vesicle for characterizing cervical cancer can be detected with one ormore binding agents including, but not limited to, Galectin-9 and/orHPVE7 aptamer, or any combination thereof.

A vesicle for characterizing endometrial cancer can be detected with oneor more binding agents including, but not limited to, Galectin-1 or anycombinations of binding agents specific for endometrial cancer.

A vesicle for characterizing head and neck cancer can be detected withone or more binding agents including, but not limited to, (111) In-cMAbU36, anti-LOXL4, U36, BIWA-1, BIWA-2, BIWA-4, or BIWA-8, or anycombination thereof.

A vesicle for characterizing IBD can be detected with one or morebinding agents including, but not limited to, ACCA (anti-glycan Ab),ALCA (anti-glycan Ab), or AMCA (anti-glycan Ab), or any combinationthereof.

A vesicle for characterizing diabetes can be detected with one or morebinding agents including, but not limited to, RBP4 aptamer or anycombination of binding agents specific for diabetes.

A vesicle for characterizing fibromyalgia can be detected with one ormore binding agents including, but not limited to, L-selectin or anycombination of binding agents specific for fibromyalgia.

A vesicle for characterizing multiple sclerosis (MS) can be detectedwith one or more binding agents including, but not limited to,Natalizumab (Tysabri) or any combination of binding agents specific forMS.

In addition, a vesicle for characterizing rheumatic disease can bedetected with one or more binding agents including, but not limited to,Rituximab (anti-CD20 Ab) and/or Keliximab (anti-CD4 Ab), or anycombination of binding agents specific for rheumatic disease.

A vesicle for characterizing Alzheimer disease can be detected with oneor more binding agents including, but not limited to, TH14-BACE1aptapers, S10-BACE1 aptapers, anti-Abeta, Bapineuzumab (AAB-001)-Elan,LY2062430 (anti-amyloid beta Ab)-Eli Lilly, or BACE1-Anti sense, or anycombination thereof.

A vesicle for characterizing Prion specific diseases can be detectedwith one or more binding agents including, but not limited to, rhuPrP(c)aptamer, DP7 aptamer, Thioaptamer 97, SAF-93 aptamer, 15B3 (anti-PrPScAb), monoclonal anti PrPSc antibody P1:1, 1.5D7, 1.6F4 Abs, mab 14D3,mab 4F2, mab 8G8, or mab 12F10, or any combination thereof.

A vesicle for characterizing sepsis can be detected with one or morebinding agents including, but not limited to, HA-1A mAb, E-5 mAb,TNF-alpha MAb, Afelimomab, or E-selectin, or any combination thereof.

A vesicle for characterizing schizophrenia can be detected with one ormore binding agents including, but not limited to, L-selectin and/orN-CAM, or any combination of binding agents specific for schizophrenia.

A vesicle for characterizing depression can be detected with one or morebinding agents including, but not limited to, GPIb or any combination ofbinding agents specific for depression.

A vesicle for characterizing GIST can be detected with one or morebinding agents including, but not limited to, ANTI-DOG1 Ab or anycombination of binding agents specific for GIST.

A vesicle for characterizing esophageal cancer can be detected with oneor more binding agents including, but not limited to, CaSR binding agentor any combination of binding agents specific for esophageal cancer.

A vesicle for characterizing gastric cancer can be detected with one ormore binding agents including, but not limited to, Calpain nCL-2 bindingagent and/or drebrin binding agent, or any combination of binding agentsspecific for gastric cancer.

A vesicle for characterizing COPD can be detected with one or morebinding agents including, but not limited to, CXCR3 binding agent, CCR5binding agent, or CXCR6 binding agent, or any combination of bindingagents specific for COPD.

A vesicle for characterizing asthma can be detected with one or morebinding agents including, but not limited to, VIP binding agent, PACAPbinding agent, CGRP binding agent, NT3 binding agent, YKL-40 bindingagent, S-nitrosothiols, SCCA2 binding agent, PAI binding agent,amphiregulin binding agent, or Periostin binding agent, or anycombination of binding agents specific for asthma.

A vesicle for characterizing vulnerable plaque can be detected with oneor more binding agents including, but not limited to, Gd-DTPA-g-mimRGD(Alpha v Beta 3 integrin binding peptide), or MMP-9 binding agent, orany combination of binding agents specific for vulnerable plaque.

A vesicle for characterizing ovarian cancer can be detected with one ormore binding agents including, but not limited to, (90) Y-muHMFG1binding agent and/or OC125 (anti-CA125 antibody), or any combination ofbinding agents specific for ovarian cancer.

The binding agent can also be for a general vesicle biomarker, such as a“housekeeping protein” or antigen. The biomarker can be CD9, CD63, orCD81. For example, the binding agent can be an antibody for CD9, CD63,or CD81. The binding agent can also be for other proteins, such as forprostate specific or cancer specific vesicles. The binding agent can befor PCSA, PSMA, EpCam, B7H3, or STEAP. For example, the binding agentcan be an antibody for PCSA, PSMA, EpCam, B7H3, or STEAP.

Furthermore, additional cellular binding partners or binding agents maybe identified by any conventional methods known in the art, or asdescribed herein, and may additionally be used as a diagnostic,prognostic or therapy-related marker.

Biosignatures for Prostate Cancer, GI Cancer and Ovarian Cancer

As described herein, biosignatures comprising circulating biomarkers canbe used to characterize a cancer. This Section presents a non-exclusivelist of biomarkers that can be used as part of a biosignature, e.g., forprostate, GI, or ovarian cancer. In some embodiments, the circulatingbiomarkers are associated with a vesicle or with a population ofvesicles. For example, circulating biomarkers associated with vesiclescan be used to capture and/or to detect a vesicle or a vesiclepopulation. This Section presents a non-exclusive list of biomarkersthat can be used as part of a biosignature, e.g., for prostate, GI, orovarian cancer.

It will be appreciated that the biomarkers presented herein may beuseful in biosignatures for other diseases, e.g., other proliferativedisorders and cancers of other cellular or tissue origins. For example,transformation in various cell types can be due to common events, e.g.,mutation in p53 or other tumor suppressor. A biosignature comprisingcell-of-origin biomarkers and cancer biomarkers can be used to furtherassess the nature of the cancer. Biomarkers for metastatic cancer may beused with cell-of-origin biomarkers to assess a metastatic cancer. Suchbiomarkers for use with the invention include those in Dawood, Novelbiomarkers of metastatic cancer, Exp Rev Mol Diag July 2010, Vol. 10,No. 5, Pages 581-590, which publication is incorporated herein byreference in its entirety.

The biosignatures of the invention may comprise markers that areupregulated, downregulated, or have no change, depending on thereference. Solely for illustration, if the reference is a normal sample,the biosignature may indicate that the subject is normal if thesubject's biosignature is not changed compared to the reference.Alternately, the biosignature may comprise a mutated nucleic acid oramino acid sequence so that the levels of the components in thebiosignature are the same between a normal reference and a diseasedsample. In another case, the reference can be a cancer sample, such thatthe subject's biosignature indicates cancer if the subject'sbiosignature is substantially similar to the reference. The biosignatureof the subject can comprise components that are both upregulated anddownregulated compared to the reference. Solely for illustration, if thereference is a normal sample, a cancer biosignature can comprise bothupregulated oncogenes and downregulated tumor suppressors. Vesiclemarkers can also be differentially expressed in various settings. Forexample, tetraspanins may be overexpressed in cancer vesicles comparedto non-cancer vesicles, whereas MFG-E8 can be overexpressed innon-cancer vesicles as compared to cancer vesicles.

Prostate Cancer

Prostate-specific antigen (PSA) is a protein produced by the cells ofthe prostate gland. PSA is present in small quantities in the serum ofnormal men, and is often elevated in the presence of prostate cancer(PCa) and in other prostate disorders. A blood test to measure PSA iscurrently used for the screening of prostate cancer, but thiseffectiveness has also been questioned. For example, PSA levels can beincreased by prostate infection, irritation, benign prostatichyperplasia (BPH), digital rectal examination (DRE) and recentejaculation, producing a false positive result that can lead tounnecessary prostate biopsy and concomitant morbidities. BPH is a commoncause of elevated PSA levels. PSA may indicate whether there issomething wrong with the prostate, but it cannot effectivelydifferentiate between BPH and PCa. PCA3, a transcript found to beoverexpressed by prostate cancer cells, is thought to be slightly morespecific for PCa, but this depends on the cutoffs used for PSA and PCA3,as well as the populations studied.

The invention provides circulating biomarkers can be used to distinguishBPH and PCa. A biomarker panel is assessed to distinguish BPH from PCa.The panel can be used to detect vesicles displaying certain surfacemarkers. In some embodiments, the surface markers comprise one or moreof BCMA, CEACAM-1, HVEM, IL-1 R4, IL-10 Rb and Trappin-2. The levels ofthe biomarkers in vesicles derived from blood samples can be assayed andthen used to distinguish BPH from PCa.

In another aspect, microRNAs (miRs) are used to differentiate betweenBPH and prostate cancer. The miRs can be isolated directly from apatient sample, and/or vesicles derived from patient samples can beanalyzed for miR payload contained within the vesicles. The sample canbe a bodily fluid, including semen, urine, blood, serum or plasma. Thesample can also comprise a tissue or biopsy sample. A number ofdifferent methodologies are available for detecting miRs as describedherein. In some embodiments, arrays of miR panels are use tosimultaneously query the expression of multiple miRs. For example, theExiqon mIRCURY LNA microRNA PCR system panel (Exiqon, Inc., Woburn,Mass.) can be used for such purposes. miRs that distinguish BPH and PCacan be overexpressed in BPH samples as compared to PCa samples,including without limitation one or more of: hsa-miR-329, hsa-miR-30a,hsa-miR-335, hsa-miR-152, hsa-miR-151-5p, hsa-miR-200a and hsa-miR-145.Alternately, miRs that distinguish BPH and PCa can be overexpressed inPCa samples versus BPH samples, including without limitation one or moreof: hsa-miR-29a, hsa-miR-106b, hsa-miR-595, hsa-miR-142-5p, hsa-miR-99a,hsa-miR-20b, hsa-miR-373, hsa-miR-502-5p, hsa-miR-29b, hsa-miR-142-3p,hsa-miR-663, hsa-miR-423-5p, hsa-miR-15a, hsa-miR-888, hsa-miR-361-3p,hsa-miR-365, hsa-miR-10b, hsa-miR-199a-3p, hsa-miR-181a, hsa-miR-19a,hsa-miR-125b, hsa-miR-760, hsa-miR-7a, hsa-miR-671-5p, hsa-miR-7c,hsa-miR-1979, and hsa-miR-103.

The expression levels of one or more of the above miRs can be assessedand compared to reference levels to detect miRs that are differentiallyexpressed, thereby providing a diagnostic, prognostic or theranosticreadout. The reference levels can be those of the miRs in exosomesderived from normal patients, e.g., patients without prostate disease.Thus, differential expression of one or more miRs from the referencelevels can indicate that the sample differs from normal, e.g., comprisesBPH or PCa. The reference levels can be those of the miRs in exosomesderived from BPH patients. Thus, differential expression of one or moremiRs from the reference levels can indicate that the sample differs fromBPH, e.g., comprises normal or PCa. The reference levels can be those ofthe miRs in exosomes derived from PCa patients. Thus, differentialexpression of one or more miRs from the reference levels can indicatethat the sample differs from PCa, e.g., comprises normal or BPH.

In some embodiments, the level of one or more miR in the test sample arecorrelated with the level of the same miRs in a reference sample,thereby providing a diagnostic, prognostic or theranostic readout. Thereference sample can comprise the miR levels of one or more samples withBPH, PCa, or can be from normals without BPH or PCa. When the level ofone or more miR in the test sample correlates most closely with that ofthe normal reference levels, the test sample can be classified asnormal. When the level of one or more miR in the test sample correlatesmost closely with that of the BPH reference levels, the test sample canbe classified as BPH. When the level of one or more miR in the testsample correlates most closely with that of the PCa reference levels,the test sample can be classified as PCa.

A biosignature can be used to characterize prostate cancer. As describedabove, a biosignature for prostate cancer can comprise a binding agentassociated with prostate cancer (for example, as shown in FIG. 2), andone or more additional biomarkers, such as shown in FIG. 19. Forexample, a biosignature for prostate cancer can comprise a binding agentto PSA, PSMA, TMPRSS2, mAB 5D4, XPSM-A9, XPSM-A10, Galectin-3,E-selectin, Galectin-1, E4 (IgG2a kappa), or any combination thereof,with one or more additional biomarkers, such as one or more miRNA, oneor more DNA, one or more additional peptide, protein, or antigenassociated with prostate cancer, such as, but not limited to, thoseshown in FIG. 19.

A biosignature for prostate cancer can comprise an antigen associatedwith prostate cancer (for example, as shown in FIG. 1), and one or moreadditional biomarkers, such as shown in FIG. 19. A biosignature forprostate cancer can comprise one or more antigens associated withprostate cancer, such as, but not limited to, KIA1, intact fibronectin,PSA, TMPRSS2, FASLG, TNFSF10, PSMA, NGEP, IL-7RI, CSCR4, CysLT1R, TRPM8,Kv1.3, TRPV6, TRPM8, PSGR, MISIIR, or any combination thereof. Thebiosignature for prostate cancer can comprise one or more of theaforementioned antigens and one or more additional biomarkers, such as,but not limited to miRNA, mRNA, DNA, or any combination thereof.

A biosignature for prostate cancer can also comprise one or moreantigens associated with prostate cancer, such as, but not limited to,KIA1, intact fibronectin, PSA, PCA3, TMPRSS2, TMPRSS2-ERG, FASLG,TNFSF10, PSMA, NGEP, IL-7RI, CSCR4, CysLT1R, TRPM8, Kv1.3, TRPV6, TRPM8,PSGR, MISIIR, or any combination thereof, and one or more miRNAbiomarkers, such as, but not limited to, miR-202, miR-210, miR-296,miR-320, miR-370, miR-373, miR-498, miR-503, miR-184, miR-198, miR-302c,miR-345, miR-491, miR-513, miR-32, miR-182, miR-31, miR-26a-1/2,miR-200c, miR-375, miR-196a-1/2, miR-370, miR-425, miR-425, miR-194-1/2,miR-181a-1/2, miR-34b, let-7i, miR-188, miR-25, miR-106b, miR-449,miR-99b, miR-93, miR-92-1/2, miR-125a, miR-141, let-7a, let-7b, let-7c,let-7d, let-7g, miR-16, miR-23a, miR-23b, miR-26a, miR-92, miR-99a,miR-103, miR-125a, miR-125b, miR-143, miR-145, miR-195, miR-199,miR-221, miR-222, miR-497, let-7f, miR-19b, miR-22, miR-26b, miR-27a,miR-27b, miR-29a, miR-29b, miR-30_(—)5p, miR-30c, miR-100, miR-141,miR-148a, miR-205, miR-520h, miR-494, miR-490, miR-133a-1, miR-1-2,miR-218-2, miR-220, miR-128a, miR-221, miR-499, miR-329, miR-340,miR-345, miR-410, miR-126, miR-205, miR-7-1/2, miR-145, miR-34a,miR-487, or let-7b, or any combination thereof.

A biosignature for prostate cancer can also comprise one or morecirculating biomarkers, such as microRNAs associated with prostatecancer, including those described in Brase et al., Circulating miRNAsare correlated with tumor progression in prostate cancer. Int J Cancer.2011 Feb. 1; 128(3):608-16; Wach et al., MiRNA profiles of prostatecarcinoma detected by multi-platform miRNA screening. Int J Cancer. 2011Mar. 11. doi: 10.1002/ijc.26064; Gordanpour et al., miR-221 IsDown-regulated in TMPRSS2:ERG Fusion-positive Prostate Cancer.Anticancer Res. 2011 February; 31(2):403-10; Hagman et al., miR-34c isdownregulated in prostate cancer and exerts tumor suppressive functions.Int J Cancer. 2010 Dec. 15; 127(12):2768-76; Sun et al., miR-99 Familyof MicroRNAs Suppresses the Expression of Prostate-Specific Antigen andProstate Cancer Cell Proliferation. Cancer Res. 2011 Feb. 15;71(4):1313-24; Bao et al., Polymorphisms inside MicroRNAs and MicroRNATarget Sites Predict Clinical Outcomes in Prostate Cancer PatientsReceiving Androgen-Deprivation Therapy. Clin Cancer Res. 2011 Feb. 15;17(4):928-936; Moltzahn et al., Microfluidic-based multiplex qRT-PCRidentifies diagnostic and prognostic microRNA signatures in the sera ofprostate cancer patients. Cancer Res. 2011 Jan. 15; 71(2):550-60;Carlsson et al., Validation of suitable endogenous control genes forexpression studies of miRNA in prostate cancer tissues. Cancer GenetCytogenet. 2010 Oct. 15; 202(2):71-75; Zhang et al., Serum miRNA-21:elevated levels in patients with metastatic hormone-refractory prostatecancer and potential predictive factor for the efficacy ofdocetaxel-based chemotherapy. Prostate. 2011 Feb. 15; 71(3):326-31;Majid et al., MicroRNA-205-directed transcriptional activation of tumorsuppressor genes in prostate cancer. Cancer. 2010 Dec. 15;116(24):5637-49; Kojima et al., MiR-34a attenuates paclitaxel-resistanceof hormone-refractory prostate cancer PC3 cells through direct andindirect mechanisms. Prostate. 2010 Oct. 1; 70(14):1501-12; Lewinshteinet al., Genomic predictors of prostate cancer therapy outcomes. ExpertRev Mol Diagn. 2010 July; 10(5):619-36; each of which publication ishereby incorporated by reference in its entirety.

Furthermore, the miRNA for a prostate cancer biosignature can be a miRNAthat interacts with PFKFB3, RHAMM (HMMR), cDNA FLJ42103, ASPM, CENPF,NCAPG, Androgen Receptor, EGFR, HSP90, SPARC, DNMT3B, GART, MGMT, SSTR3,TOP2B, or any combination thereof, such as those described herein anddepicted in FIG. 60. The miRNA can also be miR-9, miR-629, miR-141,miR-671-3p, miR-491, miR-182, miR-125a-3p, miR-324-5p, miR-148B,miR-222, or any combination thereof.

The biosignature for prostate cancer can comprise one or more antigensassociated with prostate cancer, such as, but not limited to, KIA1,intact fibronectin, PSA, TMPRSS2, FASLG, TNFSF10, PSMA, NGEP, IL-7RI,CSCR4, CysLT1R, TRPM8, Kv1.3, TRPV6, TRPM8, PSGR, MISIIR, or anycombination thereof, and one or more additional biomarkers such as, butnot limited to, the aforementioned miRNAs, mRNAs (such as, but notlimited to, AR or PCA3), snoRNA (such as, but not limited to, U50) orany combination thereof.

The biosignature can also comprise one or more gene fusions, such asACSL3-ETV1, C15ORF21-ETV1, FLJ35294-ETV1, HERV-ETV1, TMPRSS2-ERG,TMPRSS2-ETV1/4/5, TMPRSS2-ETV4/5, SLC5A3-ERG, SLC5A3-ETV1, SLC5A3-ETV5or KLK2-ETV4.

A vesicle can be isolated, assayed, or both, for one or more miRNA andone or more antigens associated with prostate cancer to provide adiagnostic, prognostic or theranostic profile, such as the stage of thecancer, the efficacy of the cancer, or other characteristics of thecancer. Alternatively, the vesicle can be directly assayed from asample, such that the vesicle is not purified or concentrated prior toassaying for one or more miRNA or antigens associated with prostatecancer.

As depicted in FIG. 68, a prostate cancer biosignature can compriseassaying EpCam, CD63, CD81, CD9, or any combination thereof, of avesicle. The prostate cancer biosignature can comprise detection ofEpCam, CD9, CD63, CD81, PCSA or any combination thereof. For example,the prostate cancer biosignature can comprise EpCam, CD9, CD63 and CD81or PCSA, CD9, CD63 and CD81 (see for example, FIG. 70A). The prostatecancer biosignature can also comprise PCSA, PSMA, B7H3, or anycombination thereof (see for example, FIG. 70B).

Furthermore, assessing a plurality of biomarkers can provide increasedsensitivity, specificity, or signal intensity, as compared to assessingless than a plurality of biomarkers. For example, assessing PSMA andB7H3 can provide increased sensitivity in detection as compared toassessing PSMA or B7H3 alone. Assessing CD9 and CD63 can provideincreased sensitivity in detection as compared to assessing CD9 or CD63alone. In one embodiment, one or more of the following biomarkers aredetected: EpCam, CD9, PCSA, CD63, CD81, PSMA, B7H3, PSCA, ICAM, STEAP,and EGFR. In another embodiment, EpCam+, CK+, CD45− vesicles aredetected.

Prostate cancer can also be characterized based on meeting at least 1,2, 3, 4, 5, 6, 7, 8, 9, or 10 criteria. For example, a number ofdifferent criteria can be used: 1) if the amount of vesicles in a samplefrom a subject is higher than a reference value; 2) if the amount ofprostate cell derived vesicles is higher than a reference value; and 3)if the amount of vesicles with one or more cancer specific biomarkers ishigher than a reference value, the subject is diagnosed with prostatecancer. The method can further include a quality control measure.

In another embodiment, one or more biosignatures of a vesicle is usedfor the diagnosis between normal prostate and prostate cancer, orbetween normal prostate, BPH and PCa. Any appropriate biomarkerdisclosed herein can be used to distinguish PCa. In some embodiments,one or more general capture agents to a biomarker (or capture biomarker,a biomarker that is detected or bound by a capture agent) can be used tocapture one or more vesicles from a sample from a subject.

Prostate specific biomarkers can be used to identify prostate specificvesicles. Cancer biomarkers can be used to identify cancer specificvesicles. In some embodiments, one or more of CD9, CD81 and CD63 areused as capture biomarkers. In some embodiments, PCSA is used as aprostate biomarker. In some embodiments, the one or more cancerbiomarkers comprise one or more of EpCam and B7H3. Additional biomarkersthat can distinguish PCa from normal include ICAM1, EGFR, STEAP1 andPSCA.

In some embodiments, the method of identifying prostate cancer in asubject comprises: (a) capturing a population of vesicles in a samplefrom the subject using a capture agent; (b) determining a level of oneor more cancer biomarkers in the population of vesicles; (c) determininga level of one or more prostate biomarkers in the population ofvesicles; and (d) identifying the subject as having prostate cancer ifthe level of the one or more cancer biomarkers and the level of one ormore prostate biomarkers meet a predetermined threshold value. In someembodiments, the capture agent comprises one or more binding agents forCD9, CD81 and CD63. In some embodiments, the one or more prostatebiomarker comprises PCSA and/or PSMA. In some embodiments, the one ormore cancer biomarkers comprise one or more of EpCam and B7H3. In otherembodiments, binding agents to the one or more prostate and/or cancerbiomarkers are used as capture agents and binding agents to the one ormore general vesicle markers are used as detection agents. In someembodiments, the predetermined threshold value comprises a measuredvalue of a detectable label. For example, the detectable label can be afluorescent moiety and the value can be a luminscence value of themoeity.

In another embodiment, the prognosis of prostate cancer is determined bydetecting EpCam, CK (cytokeratin), and/or CD45 expression. In anembodiment, a poor prognosis is provided when EpCam and CK are detectedor detected at a high levels, and detection of CD45 is low or absent(ie. a vesicle that is EpCam+, CK+, CD45−).

The methods of the invention can be used to distinguish a stage or gradeof a prostate cancer. Prostate cancer staging is a process ofcategorizing the risk of cancer spread beyond the prostate. Such spreadis related to the probability of being cured with local therapies suchas surgery or radiation. The information considered in such prognosticclassification is based on clinical and pathological factors, includingphysical examination, imaging studies, blood tests and/or biopsyexamination.

The most common scheme used to stage prostate cancer is promulgated bythe American Joint Committee on Cancer, and is referred to as the TNMsystem. The TNM system evaluates the size of the tumor, the extent ofinvolved lymph nodes, metastasis and also takes into account cancergrade. As with many other cancers, the cancers are often grouped bystage, e.g., stages I-IV). Generally, Stage I disease is cancer that isfound incidentally in a small part of the sample when prostate tissuewas removed for other reasons, such as benign prostatic hypertrophy, andthe cells closely resemble normal cells and the gland feels normal tothe examining finger. In Stage II more of the prostate is involved and alump can be felt within the gland. In Stage III, the tumor has spreadthrough the prostatic capsule and the lump can be felt on the surface ofthe gland. In Stage IV disease, the tumor has invaded nearby structures,or has spread to lymph nodes or other organs.

The Whitmore-Jewett stage is another staging scheme that is now usedless often. The Gleason Grading System is based on cellular content andtissue architecture from biopsies, which provides an estimate of thedestructive potential and ultimate prognosis of the disease.

The TNM tumor classification system can be used to describe the extentof cancer in a subject's body. T describes the size of the tumor andwhether it has invaded nearby tissue, N describes regional lymph nodesthat are involved, and M describes distant metastasis. TNM is maintainedby the International Union Against Cancer (UICC) and is used by theAmerican Joint Committee on Cancer (AJCC) and the InternationalFederation of Gynecology and Obstetrics (FIGO). Those of skill in theart understand that not all tumors have TNM classifications such as,e.g., brain tumors. Generally, T (a,is,(0), 1-4) is measured as the sizeor direct extent of the primary tumor. N (0-3) refers to the degree ofspread to regional lymph nodes: N0 means that tumor cells are absentfrom regional lymph nodes, N1 means that tumor cells spread to theclosest or small numbers of regional lymph nodes, N2 means that tumorcells spread to an extent between N1 and N3; N3 means that tumor cellsspread to most distant or numerous regional lymph nodes. M (0/1) refersto the presence of metastasis: MX means that distant metastasis was notassessed; M0 means that no distant metastasis are present; M1 means thatmetastasis has occurred to distant organs (beyond regional lymph nodes).M1 can be further delineated as follows: M1a indicates that the cancerhas spread to lymph nodes beyond the regional ones; M1b indicates thatthe cancer has spread to bone; and M1c indicates that the cancer hasspread to other sites (regardless of bone involvement). Other parametersmay also be assessed. G (1-4) refers to the grade of cancer cells (i.e.,they are low grade if they appear similar to normal cells, and highgrade if they appear poorly differentiated). R (0/1/2) refers to thecompleteness of an operation (i.e., resection-boundaries free of cancercells or not). L (0/1) refers to invasion into lymphatic vessels. V(0/1) refers to invasion into vein. C (1-4) refers to a modifier of thecertainty (quality) of V.

Prostate tumors are often assessed using the Gleason scoring system. TheGleason scoring system is based on microscopic tumor patterns assessedby a pathologist while interpreting a biopsy specimen. When prostatecancer is present in the biopsy, the Gleason score is based upon thedegree of loss of the normal glandular tissue architecture (i.e. shape,size and differentiation of the glands). The classic Gleason scoringsystem has five basic tissue patterns that are technically referred toas tumor “grades.” The microscopic determination of this loss of normalglandular structure caused by the cancer is represented by a grade, anumber ranging from 1 to 5, with 5 being the worst grade. Grade 1 istypically where the cancerous prostate closely resembles normal prostatetissue. The glands are small, well-formed, and closely packed. At Grade2 the tissue still has well-formed glands, but they are larger and havemore tissue between them, whereas at Grade 3 the tissue still hasrecognizable glands, but the cells are darker. At high magnification,some of these cells in a Grade 3 sample have left the glands and arebeginning to invade the surrounding tissue. Grade 4 samples have tissuewith few recognizable glands and many cells are invading the surroundingtissue. For Grade 5 samples, the tissue does not have recognizableglands, and are often sheets of cells throughout the surrounding tissue.

miRs that distinguish metastatic and non-metastatic prostate cancer canbe overexpressed in metastatic samples versus non-metastatic.Alternately, miRs that distinguish metastatic and non-metastaticprostate cancer can be overexpressed in non-metastatic samples versusmetastatic. Useful miRs for distinguishing metastatic prostate cancerinclude one or more of miR-495, miR-10a, miR-30a, miR-570, miR-32,miR-885-3p, miR-564, and miR-134. In another embodiment, miRs fordistinguishing metastatic prostate cancer include one or more ofhsa-miR-375, hsa-miR-452, hsa-miR-200b, hsa-miR-146b-5p, hsa-miR-1296,hsa-miR-17*, hsa-miR-100, hsa-miR-574-3p, hsa-miR-20a*, hsa-miR-572,hsa-miR-1236, hsa-miR-181a, hsa-miR-937, and hsa-miR-23a*. In stillanother embodiment, useful miRs for distinguishing metastatic prostatecancer include one or more of hsa-miR-200b, hsa-miR-375, hsa-miR-582-3p,hsa-miR-17*, hsa-miR-1296, hsa-miR-20a*, hsa-miR-100, hsa-miR-452, andhsa-miR-577. The miRs for distinguishing metastatic prostate cancer canbe one or more of miR-141, miR-375, miR-200b and miR-574-3p.

In another aspect, microRNAs (miRs) are used to differentiate betweencancer and non-cancer samples. Useful miRs for distinguishing cancerfrom non-cancer include one or more of hsa-miR-574-3p, hsa-miR-331-3p,hsa-miR-326, hsa-miR-181a-2*, hsa-miR-130b, hsa-miR-301a, hsa-miR-141,hsa-miR-432, hsa-miR-107, hsa-miR-628-5p, hsa-miR-625*, hsa-miR-497, andhsa-miR-484. In another embodiment, useful miRs for distinguishingcancer from non-cancer include one or more of hsa-miR-574-3p,hsa-miR-141, hsa-miR-331-3p, hsa-miR-432, hsa-miR-326, hsa-miR-2110,hsa-miR-107, hsa-miR-130b, hsa-miR-301a, and hsa-miR-625*. In stillanother embodiment, the useful miRs for distinguishing cancer fromnon-cancer include one or more of hsa-miR-107, hsa-miR-326, hsa-miR-432,hsa-miR-574-3p, hsa-miR-625*, hsa-miR-2110, hsa-miR-301a, hsa-miR-141 orhsa-miR-373*. The biosignature for distinguishing cancer from non-cancercan comprise one or more of miR-148a, miR-122, miR-146a, miR-22, andmiR-24.

The biosignatures of the invention can comprise multiple markers. Forexample, multiple protein markers and miRs can be used to distinguishprostate cancer from normal, BPH and PCa, or metastatic versusnon-metastatic disease. In this manner, improved sensitivity,specificity, and/or accuracy can be obtained. In some embodiments, thelevels of one or more of hsa-miR-432, hsa-miR-143, hsa-miR-424,hsa-miR-204, hsa-miR-581f and hsa-miR-451 are detected in a patientsample to assess the presence of prostate cancer. Any of these miRs canbe elevated in patients with PCa but having serum PSA <4.0 ng/ml. In anembodiment, the invention provides a method of assessing a prostatecancer, comprising determining a level of one or more of hsa-miR-432,hsa-miR-143, hsa-miR-424, hsa-miR-204, hsa-miR-581f and hsa-miR-451 in asample from a subject. The sample can be a bodily fluid, e.g., blood,plasma or serum. The miRs can be isolated in vesicles isolated from thesample. The subject can have a PSA level less than some threshold, suchas 2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, 3.6, 3.8, 4.0, 4.2, 4.4, 4.6,4.8, 5.0, 5.2, 5.4, 5.6, 5.8, or 6.0 ng/ml in a blood sample. Higherlevels of the miRs than in a reference sample can indicate the presenceof PCa in the sample. In some embodiments, the reference comprises alevel of the one or more miRs in control samples from subjects withoutPCa. In some embodiments, the reference comprises a level of the one ormore miRs in control samples from subject with PCa and PSA levels ≧somethreshold, such as 2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, 3.6, 3.8,4.0, 4.2, 4.4, 4.6, 4.8, 5.0, 5.2, 5.4, 5.6, 5.8, or 6.0 ng/ml. Thethreshold can be 4.0 ng/ml.

The invention provides for assessing a prostate disorder comprisingdetecting a presence or level of one or more circulating biomarkerselected from the biomarkers listed above. The one or more circulatingbiomarker can also be selected from BCMA, CEACAM-1, HVEM, IL-1 R4, IL-10Rb, Trappin-2, p53, hsa-miR-103, hsa-miR-106b, hsa-miR-10b,hsa-miR-125b, hsa-miR-142-3p, hsa-miR-142-5p, hsa-miR-145,hsa-miR-151-5p, hsa-miR-152, hsa-miR-15a, hsa-miR-181a, hsa-miR-1979,hsa-miR-199a-3p, hsa-miR-19a, hsa-miR-200a, hsa-miR-20b, hsa-miR-29a,hsa-miR-29b, hsa-miR-30a, hsa-miR-329, hsa-miR-335, hsa-miR-361-3p,hsa-miR-365, hsa-miR-373, hsa-miR-423-5p, hsa-miR-502-5p, hsa-miR-595,hsa-miR-663, hsa-miR-671-5p, hsa-miR-760, hsa-miR-7a, hsa-miR-7c,hsa-miR-888, hsa-miR-99a, and a combination thereof. The one or morecirculating biomarkers can be selected from the following: hsa-miR-100,hsa-miR-1236, hsa-miR-1296, hsa-miR-141, hsa-miR-146b-5p, hsa-miR-17*,hsa-miR-181a, hsa-miR-200b, hsa-miR-20a*, hsa-miR-23a*, hsa-miR-331-3p,hsa-miR-375, hsa-miR-452, hsa-miR-572, hsa-miR-574-3p, hsa-miR-577,hsa-miR-582-3p, hsa-miR-937, miR-10a, miR-134, miR-141, miR-200b,miR-30a, miR-32, miR-375, miR-495, miR-564, miR-570, miR-574-3p,miR-885-3p, and a combination thereof. Further still, the one or morecirculating biomarkers can be selected from the following: hsa-let-7b,hsa-miR-107, hsa-miR-1205, hsa-miR-1270, hsa-miR-130b, hsa-miR-141,hsa-miR-143, hsa-miR-148b*, hsa-miR-150, hsa-miR-154*, hsa-miR-181a*,hsa-miR-181a-2*, hsa-miR-18a*, hsa-miR-19b-1*, hsa-miR-204,hsa-miR-2110, hsa-miR-215, hsa-miR-217, hsa-miR-219-2-3p, hsa-miR-23b*,hsa-miR-299-5p, hsa-miR-301a, hsa-miR-301a, hsa-miR-326, hsa-miR-331-3p,hsa-miR-365*, hsa-miR-373*, hsa-miR-424, hsa-miR-424*, hsa-miR-432,hsa-miR-450a, hsa-miR-451, hsa-miR-484, hsa-miR-497, hsa-miR-517*,hsa-miR-517a, hsa-miR-518f, hsa-miR-574-3p, hsa-miR-595, hsa-miR-617,hsa-miR-625*, hsa-miR-628-5p, hsa-miR-629, hsa-miR-634, hsa-miR-769-5p,hsa-miR-93, hsa-miR-96. The circulating biomarkers can be one or more ofhsa-miR-1974, hsa-miR-27b, hsa-miR-103, hsa-miR-146a, hsa-miR-22,hsa-miR-382, hsa-miR-23a, hsa-miR-376c, hsa-miR-335, hsa-miR-142-5p,hsa-miR-221, hsa-miR-142-3p, hsa-miR-151-3p, hsa-miR-21 and hsa-miR-16.In an embodiment, the circulating biomarkers comprise one or more ofCD9, PSMA, PCSA, CD63, CD81, B7H3, IL 6, OPG-13, IL6R, PA2G4, EZH2,RUNX2, SERPINB3, and EpCam. The biomarkers can comprise one or more ofFOX01A, SOX9, CLNS1A, PTGDS, XPO1, LETMD1, RAD23B, ABCC3, APC, CHES1,EDNRA, FRZB, HSPG2, and TMPRSS2_ETV1 fusion. See WO2010056993, whichapplication is incorporated by reference herein in its entirety. Inanother embodiment, the circulating biomarkers comprise one or more ofA33, a33 n15, AFP, ALA, ALIX, ALP, AnnexinV, APC, ASCA, ASPH (246-260),ASPH (666-680), ASPH (A-10), ASPH (D01P), ASPH (D03), ASPH (G-20),ASPH(H-300), AURKA, AURKB, B7H3, B7H4, BCA-225, BCNP1, BDNF, BRCA, CA125(MUC16), CA-19-9, C-Bir, CD1.1, CD10, CD174 (Lewis y), CD24, CD44, CD46,CD59 (MEM-43), CD63, CD66e CEA, CD73, CD81, CD9, CDA, CDAC1 1a2, CEA,C-Erb2, C-erbB2, CRMP-2, CRP, CXCL12, CYFRA21-1, DLL4, DR3, EGFR, Epcam,EphA2, EphA2 (H-77), ER, ErbB4, EZH2, FASL, FRT, FRT c.f23, GDF15, GPCR,GPR30, Gro-alpha, HAP, HBD 1, HBD2, HER 3 (ErbB3), HSP, HSP70, hVEGFR2,iC3b, IL6 Unc, IL-1B, IL6 Unc, IL6R, IL8, IL-8, INSIG-2, KLK2, L1CAM,LAMN, LDH, MACC-1, MAPK4, MART-1, MCP-1, M-CSF, MFG-E8, MIC1, MIF, MISRII, MMG, MMP26, MMP7, MMP9, MS4A1, MUC1, MUC1 seq1, MUC1 seq11A, MUC17,MUC2, Ncam, NGAL, NPGP/NPFF2, OPG, OPN, p53, p53, PA2G4, PBP, PCSA,PDGFRB, PGP9.5, PIM1, PR (B), PRL, PSA, PSMA, PSME3, PTEN, R5-CD9 Tube1, Reg IV, RUNX2, SCRN1, seprase, SERPINB3, SPARC, SPB, SPDEF, SRVN,STAT 3, STEAP1, TF (FL-295), TFF3, TGM2, TIMP-1, TIMP1, TIMP2, TMEM211,TMPRSS2, TNF-alpha, Trail-R2, Trail-R4, TrKB, TROP2, Tsg 101, TWEAK,UNC93A, VEGF A, and YPSMA-1. Any combination of these markers can beused in a biosignature to assess a prostate cancer. The circulatingbiomarkers can be associated with vesicles, e.g., vesicle surfacemarkers or vesicle payload. The prostate disorders include withoutlimitation a benign disorder such as BPH, or prostate cancer, includingcancers of various stages and grades. See, e.g., Table 5.

TABLE 5 Biomarkers for Prostate Disorders Illustrative DisorderBiomarkers Benign Prostate BCMA, CEACAM-1, HVEM, IL-1 R4, IL-10 Rb,Trappin-2, p53, hsa-miR-329, Hyperplasia (BPH) hsa-miR-30a, hsa-miR-335,hsa-miR-152, hsa-miR-151-5p, hsa-miR-200a, hsa- miR-145, hsa-miR-29a,hsa-miR-106b, hsa-miR-595, hsa-miR-142-5p, hsa-miR- 99a, hsa-miR-20b,hsa-miR-373, hsa-miR-502-5p, hsa-miR-29b, hsa-miR-142-3p, hsa-miR-663,hsa-miR-423-5p, hsa-miR-15a, hsa-miR-888, hsa-miR-361-3p, hsa- miR-365,hsa-miR-10b, hsa-miR-199a-3p, hsa-miR-181a, hsa-miR-19a, hsa-miR- 125b,hsa-miR-760, hsa-miR-7a, hsa-miR-671-5p, hsa-miR-7c, hsa-miR-1979,hsa-miR-103 Metastatic Prostate hsa-miR-100, hsa-miR-1236, hsa-miR-1296,hsa-miR-141, hsa-miR-146b-5p, hsa- Cancer miR-17*, hsa-miR-181a,hsa-miR-200b, hsa-miR-20a*, hsa-miR-23a*, hsa-miR- 331-3p, hsa-miR-375,hsa-miR-452, hsa-miR-572, hsa-miR-574-3p, hsa-miR-577, hsa-miR-582-3p,hsa-miR-937, miR-10a, miR-134, miR-141, miR-200b, miR-30a, miR-32,miR-375, miR-495, miR-564, miR-570, miR-574-3p, miR-885-3p MetastaticProstate hsa-miR-200b, hsa-miR-375, hsa-miR-141, hsa-miR-331-3p,hsa-miR-181a, hsa- Cancer miR-574-3p Metastatic Prostate FOX01A, SOX9,CLNS1A, PTGDS, XPO1, LETMD1, RAD23B, ABCC3, APC, Cancer CHES1, EDNRA,FRZB, HSPG2, TMPRSS2_ETV1 fusion Prostate Cancer hsa-let-7b,hsa-miR-107, hsa-miR-1205, hsa-miR-1270, hsa-miR-130b, hsa-miR- 141,hsa-miR-143, hsa-miR-148b*, hsa-miR-150, hsa-miR-154*, hsa-miR-181a*,hsa-miR-181a-2*, hsa-miR-18a*, hsa-miR-19b-1*, hsa-miR-204,hsa-miR-2110, hsa-miR-215, hsa-miR-217, hsa-miR-219-2-3p, hsa-miR-23b*,hsa-miR-299-5p, hsa-miR-301a, hsa-miR-301a, hsa-miR-326, hsa-miR-331-3p,hsa-miR-365*, hsa- miR-373*, hsa-miR-424, hsa-miR-424*, hsa-miR-432,hsa-miR-450a, hsa-miR- 451, hsa-miR-484, hsa-miR-497, hsa-miR-517*,hsa-miR-517a, hsa-miR-518f, hsa-miR-574-3p, hsa-miR-595, hsa-miR-617,hsa-miR-625*, hsa-miR-628-5p, hsa-miR-629, hsa-miR-634, hsa-miR-769-5p,hsa-miR-93, hsa-miR-96 Prostate Cancer CD9, PSMA, PCSA, CD63, CD81,B7H3, IL 6, OPG-13, IL6R, PA2G4, EZH2, RUNX2, SERPINB3, EpCam ProstateCancer A33, a33 n15, AFP, ALA, ALIX, ALP, AnnexinV, APC, ASCA, ASPH(246- 260), ASPH (666-680), ASPH (A-10), ASPH (D01P), ASPH (D03), ASPH(G- 20), ASPH (H-300), AURKA, AURKB, B7H3, B7H4, BCA-225, BCNP1, BDNF,BRCA, CA125 (MUC16), CA-19-9, C-Bir, CD1.1, CD10, CD174 (Lewis y), CD24,CD44, CD46, CD59 (MEM-43), CD63, CD66e CEA, CD73, CD81, CD9, CDA, CDAC11a2, CEA, C-Erb2, C-erbB2, CRMP-2, CRP, CXCL12, CYFRA21- 1, DLL4, DR3,EGFR, Epcam, EphA2, EphA2 (H-77), ER, ErbB4, EZH2, FASL, FRT, FRT c.f23,GDF15, GPCR, GPR30, Gro-alpha, HAP, HBD 1, HBD2, HER 3 (ErbB3), HSP,HSP70, hVEGFR2, iC3b, IL 6 Unc, IL-1B, IL6 Unc, IL6R, IL8, IL-8,INSIG-2, KLK2, L1CAM, LAMN, LDH, MACC-1, MAPK4, MART-1, MCP-1, M-CSF,MFG-E8, MIC1, MIF, MIS RII, MMG, MMP26, MMP7, MMP9, MS4A1, MUC1, MUC1seq1, MUC1 seq11A, MUC17, MUC2, Ncam, NGAL, NPGP/NPFF2, OPG, OPN, p53,p53, PA2G4, PBP, PCSA, PDGFRB, PGP9.5, PIM1, PR (B), PRL, PSA, PSMA,PSME3, PTEN, R5-CD9 Tube 1, Reg IV, RUNX2, SCRN1, seprase, SERPINB3,SPARC, SPB, SPDEF, SRVN, STAT 3, STEAP1, TF (FL-295), TFF3, TGM2,TIMP-1, TIMP1, TIMP2, TMEM211, TMPRSS2, TNF-alpha, Trail-R2, Trail-R4,TrKB, TROP2, Tsg 101, TWEAK, UNC93A, VEGF A, YPSMA-1 Prostate CancerVesicle 5T4, ACTG1, ADAM10, ADAM15, ALDOA, ANXA2, ANXA6, APOA1, MarkersATP1A1, BASP1, C1orf58, C20orf114, C8B, CAPZA1, CAV1, CD151, CD2AP,CD59, CD9, CD9, CFL1, CFP, CHMP4B, CLTC, COTL1, CTNND1, CTSB, CTSZ,CYCS, DPP4, EEF1A1, EHD1, ENO1, F11R, F2, F5, FAM125A, FNBP1L, FOLH1,GAPDH, GLB1, GPX3, HIST1H1C, HIST1H2AB, HSP90AB1, HSPA1B, HSPA8, IGSF8,ITGB1, ITIH3, JUP, LDHA, LDHB, LUM, LYZ, MFGE8, MGAM, MMP9, MYH2, MYL6B,NME1, NME2, PABPC1, PABPC4, PACSIN2, PCBP2, PDCD6IP, PRDX2, PSA, PSMA,PSMA1, PSMA2, PSMA4, PSMA6, PSMA7, PSMB1, PSMB2, PSMB3, PSMB4, PSMB5,PSMB6, PSMB8, PTGFRN, RPS27A, SDCBP, SERINC5, SH3GL1, SLC3A2, SMPDL3B,SNX9, TACSTD1, TCN2, THBS1, TPI1, TSG101, TUBB, VDAC2, VPS37B, YWHAG,YWHAQ, YWHAZ Prostate Cancer Treatment hsa-miR-1974, hsa-miR-27b,hsa-miR-103, hsa-miR-146a, hsa-miR-22, hsa-miR- 382, hsa-miR-23a,hsa-miR-376c, hsa-miR-335, hsa-miR-142-5p, hsa-miR-221, hsa-miR-142-3p,hsa-miR-151-3p, hsa-miR-21, hsa-miR-16

Any combination of these markers can be used in a biosignature to assessa prostate disorder, such as BPH and prostate cancer. The biosignaturecan also be used to assess the stage or grade of the prostate cancer.

The prostate cancer can be characterizing using one or more processesdisclosed herein with at least 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,or 70% sensitivity. The prostate cancer can be characterized with atleast 80, 81, 82, 83, 84, 85, 86, or 87% sensitivity. For example, theprostate cancer can be characterized with at least 87.1, 87.2, 87.3,87.4, 87.5, 87.6, 87.7, 87.8, 87.9, 88.0, or 89% sensitivity, such aswith at least 90% sensitivity, such as at least 91, 92, 93, 94, 95, 96,97, 98, 99 or 100% sensitivity.

The prostate cancer of a subject can also be characterized with at least70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,88, 89, 90, 91, 92, 93, 94, 95, 96, or 97% specificity, such as with atleast 97.1, 97.2, 97.3, 97.4, 97.5, 97.6, 97.7, 97.8, 97.8, 97.9, 98.0,98.1, 98.2, 98.3, 98.4, 98.5, 98.6, 98.7, 98.8, 98.9, 99.0, 99.1, 99.2,99.3, 99.4, 99.5, 99.6, 99.7, 99.8, 99.9 or 100% specificity.

The prostate cancer can also be characterized with at least 70%sensitivity and at least 80, 90, 95, 99, or 100% specificity; at least80% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; atleast 85% sensitivity and at least 80, 85, 90, 95, 99, or 100%specificity; at least 86% sensitivity and at least 80, 85, 90, 95, 99,or 100% specificity; at least 87% sensitivity and at least 80, 85, 90,95, 99, or 100% specificity; at least 88% sensitivity and at least 80,85, 90, 95, 99, or 100% specificity; at least 89% sensitivity and atleast 80, 85, 90, 95, 99, or 100% specificity; at least 90% sensitivityand at least 80, 85, 90, 95, 99, or 100% specificity; at least 95%sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; atleast 99% sensitivity and at least 80, 85, 90, 95, 99, or 100%specificity; or at least 100% sensitivity and at least 80, 85, 90, 95,99, or 100% specificity.

In some embodiments, the biosignature characterizes a phenotype of asubject with at least 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81,82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, or 97%accuracy, such as with at least 97.1, 97.2, 97.3, 97.4, 97.5, 97.6,97.7, 97.8, 97.8, 97.9, 98.0, 98.1, 98.2, 98.3, 98.4, 98.5, 98.6, 98.7,98.8, 98.9, 99.0, 99.1, 99.2, 99.3, 99.4, 99.5, 99.6, 99.7, 99.8, 99.9or 100% accuracy.

In some embodiments, the biosignature characterizes a phenotype of asubject with an AUC of at least 0.70, 0.71, 0.72, 0.73, 0.74, 0.75,0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87,0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, or 0.97, such aswith at least 0.971, 0.972, 0.973, 0.974, 0.975, 0.976, 0.977, 0.978,0.978, 0.979, 0.980, 0.981, 0.982, 0.983, 0.984, 0.985, 0.986, 0.987,0.988, 0.989, 0.99, 0.991, 0.992, 0.993, 0.994, 0.995, 0.996, 0.997,0.998, 0.999 or 1.00.

Furthermore, the confidence level for determining the specificity,sensitivity, accuracy and/or AUC can be determined with at least 70, 71,72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,90, 91, 92, 93, 94, 95, 96, 97, 98, or 99% confidence.

Gastrointestinal Cancer

The gastrointestinal (GI) tract includes without limitation the oralcavity, gums, pharynx, tongue, salivary glands, esophagus, pancreas,liver, gallbladder, small intestine (duodenum, jejunum, ileum), bileduct, stomach, large intestine (cecum, colon, rectum), appendix andanus. The biosignature can be used to detect or characterize cancers ofsuch components, e.g., colorectal cancer (CRC), stomach cancer,intestinal cancer, liver cancer or esophageal cancer. A gastrointestinal(GI) tract bio-signature can comprise any one or more antigens for aslisted in FIG. 1, any one or more binding agents associated withisolating a vesicle for characterizing colon cancer (for example, asshown in FIG. 2), any one or more additional biomarkers, such as shownin FIG. 6.

Colon Cancer

Although colonoscopy is the gold standard to screen and identifycolorectal cancer (CRC), it is estimated half of patients who arerecommended for colonoscopy are not compliant. Often the lack ofcompliance is because many perceive a colonoscopy as an uncomfortableand invasive procedure. A less invasive diagnostic test that couldidentify those patients that have a blood-based biosignature indicativeof the need for detection and biopsy by colonoscopy could improvecompliance. This strategy would result in cancers being identifiedearlier and prevent disease-free individuals from undergoing anunnecessary invasive procedure. Current blood-based tests rely onincreased levels of either carcinoembryonic antigen (CEA) orcarbohydrate antigenic determinant (CA 19-9). Unfortunately, CEA and CA19-9 are neither organ-specific nor tumor-specific. The presentinvention improves upon these markers using vesicle-based detectionassays.

The present invention provides methods to identify subjects likely tohave or having CRC using a biosignature derived from a sample from thesubject. The sample can be bodily fluids such as blood, plasma or serum,or stool. The biosignature can contain circulating biomarkers, includingbiomarkers associated with vesicles. The biosignature may containmutations detected by sequencing nucleic acids, e.g., RNAs containedwithin vesicles.

In one aspect of the invention, biosignatures are derived from vesiclesisolated from plasma of patients with and without CRC. Vesicle surfaceproteins are used in a multiplex assay to capture and detect vesicles.The quantity of vesicles with significant concentrations of thesesurface proteins leads to the development of a vesicle-specificbiosignature that can differentiate CRC samples from normal. Suchvesicles present in blood plasma of CRC patients provide a signature bywhich CRC can be diagnosed as early as histological grade 1. In someembodiments, vesicles are captured with antibodies to various surfaceproteins. The capture vesicles can be detected with vesicle specificmarkers, e.g., one or more of CD9, CD81, and CD63. In some embodiments,the vesicle-based biosignature comprises measuring the level of one ormore of CD9, CD81, CD63, EpCam, EGFR, and STEAP. In some embodiments,one or more of the following markers are used to capture and/or detectvesicles: CD9, NGAL, CD81, STEAP, CD24, A33, CD66E, EPHA2, TMEM211,TROP2, TROP2, EGFR, DR3, UNC93A, MUC17, EpCAM, MUC17, CD63, B7H3. Insome embodiments, one or more of the following markers are used tocapture and/or detect vesicles: TMEM211, MUC1, GPR110 (GPCR 110), CD24,CD9, CD81, and CD63. In some embodiments, one or more of the followingmarkers are used to capture and/or detect vesicles: DR3, STEAP, epha2,TMEM211, unc93A, A33, CD24, NGAL, EpCam, MUC17, TROP2, and TETS. In someembodiments, TMEM211 is used to capture and/or detect vesicles. In someembodiments, MUC1 is used to capture and/or detect vesicles. In someembodiments, GPR110 is used to capture and/or detect vesicles. In someembodiments, CD24 is used to capture and/or detect vesicles. In someembodiments, one or more of TMEM211, MUC1, GPR110 (GPCR 110), and CD24are used to capture vesicles and one or more general vesicle marker isused to detection the captured vesicles.

In another aspect of the invention, microRNAs (miRs) associated withvesicles are used to determine a biosignature. The miRs can be derivedfrom vesicles, e.g., exosomes, isolated from a patient sample, e.g.,blood. In some embodiments, one or more of the following miRs are usedto derive a CRC biosignature: miR 92, miR 21, miR 9 and miR 491.

In still another aspect of the invention, the payload within a vesicleis assessed. KRAS and BRAF mutation screening can be used for coloncancer monitoring from tumor samples. As shown in Example 4, KRASmutations are found in RNA derived from colon cell line vesicles.Example 5 shows that KRAS can be sequenced in RNA vesicles derived fromplasma samples. In some embodiments, sequencing of KRAS and/or BRAFnucleic acid within vesicles can be used to detect CRC. The nucleic acidcan be RNA, e.g., mRNA. A CRC biosignature can comprise sequencing ofKRAS and BRAF RNA isolated from vesicles.

A colon cancer biosignature can comprise any one or more antigens forcolon cancer as listed in FIG. 1, any one or more binding agentsassociated with isolating or detecting a vesicle for characterizingcolon cancer (for example, as shown in FIG. 2), any one or moreadditional biomarkers, such as shown in FIG. 6.

The biosignature can comprise one or more miRNA selected from the groupconsisting of miR-24-1, miR-29b-2, miR-20a, miR-10a, miR-32, miR-203,miR-106a, miR-17-5p, miR-30c, miR-223, miR-126, miR-128b, miR-21,miR-24-2, miR-99b, miR-155, miR-213, miR-150, miR-107, miR-191, miR-221,miR-20a, miR-510, miR-92, miR-513, miR-19a, miR-21, miR-20, miR-183,miR-96, miR-135b, miR-31, miR-21, miR-92, miR-222, miR-181b, miR-210,miR-20a, miR-106a, miR-93, miR-335, miR-338, miR-133b, miR-346,miR-106b, miR-153a, miR-219, miR-34a, miR-99b, miR-185, miR-223,miR-211, miR-135a, miR-127, miR-203, miR-212, miR-95, or miR-17-5p, orany combination thereof. The biosignature can also comprise one or moreunderexpressed miRs such as miR-143, miR-145, miR-143, miR-126, miR-34b,miR-34c, let-7, miR-9-3, miR-34a, miR-145, miR-455, miR-484, miR-101,miR-145, miR-133b, miR-129, miR-124a, miR-30-3p, miR-328, miR-106a,miR-17-5p, miR-342, miR-192, miR-1, miR-34b, miR-215, miR-192, miR-301,miR-324-5p, miR-30a-3p, miR-34c, miR-331, miR-148b, miR-548c-5p,miR-362-3p and miR422a.

The biosignature can comprise assessing one or more genes, such asEFNB1, ERCC1, HER2, VEGF, and EGFR. A biomarker mutation for coloncancer that can be assessed in a vesicle can also include one or moremutations of EGFR, KRAS, VEGFA, B-Raf, APC, or p53. The biosignature canalso comprise one or more proteins, ligands, or peptides that can beassessed of a vesicle, such as AFRs, Rabs, ADAM10, CD44, NG2, ephrin-B1,MIF, b-catenin, Junction, plakoglobin, glalectin-4, RACK1, tetrspanin-8,FasL, TRAIL, A33, CEA, EGFR, dipeptidase 1, hsc-70, tetraspanins, ESCRT,TS, PTEN, or TOPO1.

A vesicle can be isolated and assayed for to provide a diagnostic,prognostic or theranostic profile, such as the stage of the cancer, theefficacy of the cancer, or other characteristics of the cancer.Alternatively, the esicle can be directly assayed from a sample, suchthat the vesicles are not purified or concentrated prior to assaying fora biosignature associated with colon cancer.

As depicted in FIG. 69, a GI cancer, such as colon cancer, abiosignature can comprise detection of EpCam, CD63, CD81, CD9, CD66, orany combination thereof, of a vesicle. Furthermore, a coloncancer-biosignature for various stages of cancer can comprise CD63, CD9,EpCam, or any combination thereof (see for example, FIGS. 71 and 72).For example, the biosignature can comprise CD9 and EpCam. In someembodiments, the GI cancer biosignature comprises one or more miRNAselected from the group consisting of miR-548c-5p, miR-362-3p, miR-422a,miR-597, miR-429, miR-200a, and miR-200b. These miRNAs can beoverexpressed in GI cancers, as shown in FIG. 110. The miRNA signaturecan be combined with the biomarkers listed above. The biosignatures canprovide a diagnostic, prognostic or theranostic profile, such as thestage of the cancer, the efficacy of the cancer, or othercharacteristics of the cancer.

The invention provides for assessing a gastrointestinal disordercomprising detecting a presence or level of one or more circulatingbiomarker selected from the biomarkers listed above. The one or morecirculating biomarker can also be selected from CD9, EGFR, NGAL, CD81,STEAP, CD24, A33, CD66E, EPHA2, Ferritin, GPR30, GPR110, MMP9, OPN, p53,TMEM211, TROP2, TGM2, TIMP, EGFR, DR3, UNC93A, MUC17, EpCAM, MUC1, MUC2,TSG101, CD63, B7H3, and a combination thereof. The one or morecirculating biomarkers can be selected from the following: DR3, STEAP,epha2, TMEM211, unc93A, A33, CD24, NGAL, EpCam, MUC17, TROP2, TETS, anda combination thereof. Further still, the one or more circulatingbiomarkers can be selected from the following: A33, AFP, ALIX, ALX4,ANCA, APC, ASCA, AURKA, AURKB, B7H3, BANK1, BCNP1, BDNF, CA-19-9,CCSA-2, CCSA-3&4, CD10, CD24, CD44, CD63, CD66 CEA, CD66e CEA, CD81,CD9, CDA, C-Erb2, CRMP-2, CRP, CRTN, CXCL12, CYFRA21-1, DcR3, DLL4, DR3,EGFR, Epcam, EphA2, FASL, FRT, GAL3, GDF15, GPCR (GPR110), GPR30, GRO-1,HBD 1, HBD2, HNP1-3, IL-1B, IL8, IMP3, L1CAM, LAMN, MACC-1, MGC20553,MCP-1, M-CSF, MIC1, MIF, MMP7, MMP9, MS4A1, MUC1, MUC17, MUC2, Ncam,NGAL, NNMT, OPN, p53, PCSA, PDGFRB, PRL, PSMA, PSME3, Reg IV, SCRN1,Sept-9, SPARC, SPON2, SPR, SRVN, TFF3, TGM2, TIMP-1, TMEM211, TNF-alpha,TPA, TPS, Trail-R2, Trail-R4, TrKB, TROP2, Tsg 101, TWEAK, UNC93A, andVEGFA, and a combination thereof. In an embodiment, the circulatingbiomarkers comprise one or more of miR 92, miR 21, miR 9 and miR 491,and/or one or more of hsa-miR-376c, hsa-miR-215, hsa-miR-652,hsa-miR-582-5p, hsa-miR-324-5p, hsa-miR-1296, hsa-miR-28-5p,hsa-miR-190, hsa-miR-590-5p, hsa-miR-202, and hsa-miR-195. In anotherembodiment, the circulating biomarkers comprise one or more of TMEM211,MUC1, CD24 and/or GPR110 (GPCR 110). The circulating biomarkers can beassociated with vesicles, e.g., vesicle surface markers or vesiclepayload. The gastrointestinal disorders include without limitation abenign disorder such as benign polyps, or a cancer such as colorectalcancer, including cancers of various stages and grades. See, e.g., Table6.

TABLE 6 Biomarkers for Gastrointestinal Disorders Illustrative DisorderBiomarkers Colorectal cancer CD9, EGFR, NGAL, CD81, STEAP, CD24, A33,CD66E, EPHA2, Ferritin, GPR30, GPR110, MMP9, OPN, p53, TMEM211, TROP2,TGM2, TIMP, EGFR, DR3, UNC93A, MUC17, EpCAM, MUC1, MUC2, TSG101, CD63,B7H3 Colorectal cancer DR3, STEAP, epha2, TMEM211, unc93A, A33, CD24,NGAL, EpCam, MUC17, TROP2, TETS Colorectal cancer A33, AFP, ALIX, ALX4,ANCA, APC, ASCA, AURKA, AURKB, B7H3, BANK1, BCNP1, BDNF, CA-19-9,CCSA-2, CCSA-3&4, CD10, CD24, CD44, CD63, CD66 CEA, CD66e CEA, CD81,CD9, CDA, C-Erb2, CRMP-2, CRP, CRTN, CXCL12, CYFRA21-1, DcR3, DLL4, DR3,EGFR, Epcam, EphA2, FASL, FRT, GAL3, GDF15, GPCR (GPR110), GPR30, GRO-1,HBD 1, HBD2, HNP1-3, IL-1B, IL8, IMP3, L1CAM, LAMN, MACC-1, MGC20553,MCP-1, M-CSF, MIC1, MIF, MMP7, MMP9, MS4A1, MUC1, MUC17, MUC2, Ncam,NGAL, NNMT, OPN, p53, PCSA, PDGFRB, PRL, PSMA, PSME3, Reg IV, SCRN1,Sept-9, SPARC, SPON2, SPR, SRVN, TFF3, TGM2, TIMP-1, TMEM211, TNF-alpha,TPA, TPS, Trail-R2, Trail-R4, TrKB, TROP2, Tsg 101, TWEAK, UNC93A, VEGFAColorectal cancer miR 92, miR 21, miR 9 and miR 491 Colorectal cancerTMEM211, MUC1, CD24 and/or GPR110 (GPCR 110) Colorectal cancerhsa-miR-376c, hsa-miR-215, hsa-miR-652, hsa-miR-582-5p, hsa-miR-324-5p,hsa- miR-1296, hsa-miR-28-5p, hsa-miR-190, hsa-miR-590-5p, hsa-miR-202,hsa-miR- 195 Colorectal cancer A26C1A, A26C1B, A2M, ACAA2, ACE, ACOT7,ACP1, ACTA1, ACTA2, ACTB, vesicle markers ACTBL2, ACTBL3, ACTC1, ACTG1,ACTG2, ACTN1, ACTN2, ACTN4, ACTR3, ADAM10, ADSL, AGR2, AGR3, AGRN, AHCY,AHNAK, AKR1B10, ALB, ALDH16A1, ALDH1A1, ALDOA, ANXA1, ANXA11, ANXA2,ANXA2P2, ANXA4, ANXA5, ANXA6, AP2A1, AP2A2, APOA1, ARF1, ARF3, ARF4,ARF5, ARF6, ARHGDIA, ARPC3, ARPC5L, ARRDC1, ARVCF, ASCC3L1, ASNS,ATP1A1, ATP1A2, ATP1A3, ATP1B1, ATP4A, ATP5A1, ATP5B, ATP5I, ATP5L,ATP5O, ATP6AP2, B2M, BAIAP2, BAIAP2L1, BRI3BP, BSG, BUB3, C1orf58,C5orf32, CAD, CALM1, CALM2, CALM3, CAND1, CANX, CAPZA1, CBR1, CBR3,CCT2, CCT3, CCT4, CCT5, CCT6A, CCT7, CCT8, CD44, CD46, CD55, CD59, CD63,CD81, CD82, CD9, CDC42, CDH1, CDH17, CEACAM5, CFL1, CFL2, CHMP1A,CHMP2A, CHMP4B, CKB, CLDN3, CLDN4, CLDN7, CLIC1, CLIC4, CLSTN1, CLTC,CLTCL1, CLU, COL12A1, COPB1, COPB2, CORO1C, COX4I1, COX5B, CRYZ, CSPG4,CSRP1, CST3, CTNNA1, CTNNB1, CTNND1, CTTN, CYFIP1, DCD, DERA, DIP2A,DIP2B, DIP2C, DMBT1, DPEP1, DPP4, DYNC1H1, EDIL3, EEF1A1, EEF1A2,EEF1AL3, EEF1G, EEF2, EFNB1, EGFR, EHD1, EHD4, EIF3EIP, EIF3I, EIF4A1,EIF4A2, ENO1, ENO2, ENO3, EPHA2, EPHA5, EPHB1, EPHB2, EPHB3, EPHB4,EPPK1, ESD, EZR, F11R, F5, F7, FAM125A, FAM125B, FAM129B, FASLG, FASN,FAT, FCGBP, FER1L3, FKBP1A, FLNA, FLNB, FLOT1, FLOT2, G6PD, GAPDH, GARS,GCN1L1, GDI2, GK, GMDS, GNA13, GNAI2, GNAI3, GNAS, GNB1, GNB2, GNB2L1,GNB3, GNB4, GNG12, GOLGA7, GPA33, GPI, GPRC5A, GSN, GSTP1, H2AFJ, HADHA,hCG_1757335, HEPH, HIST1H2AB, HIST1H2AE, HIST1H2AJ, HIST1H2AK, HIST1H4A,HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E, HIST1H4F, HIST1H4H, HIST1H4I,HIST1H4J, HIST1H4K, HIST1H4L, HIST2H2AC, HIST2H4A, HIST2H4B, HIST3H2A,HIST4H4, HLA-A, HLA- A29.1, HLA-B, HLA-C, HLA-E, HLA-H, HNRNPA2B1,HNRNPH2, HPCAL1, HRAS, HSD17B4, HSP90AA1, HSP90AA2, HSP90AA4P, HSP90AB1,HSP90AB2P, HSP90AB3P, HSP90B1, HSPA1A, HSPA1B, HSPA1L, HSPA2, HSPA4,HSPA5, HSPA6, HSPA7, HSPA8, HSPA9, HSPD1, HSPE1, HSPG2, HYOU1, IDH1,IFITM1, IFITM2, IFITM3, IGH@, IGHG1, IGHG2, IGHG3, IGHG4, IGHM,IGHV4-31, IGK@, IGKC, IGKV1-5, IGKV2-24, IGKV3-20, IGSF3, IGSF8, IQGAP1,IQGAP2, ITGA2, ITGA3, ITGA6, ITGAV, ITGB1, ITGB4, JUP, KIAA0174,KIAA1199, KPNB1, KRAS, KRT1, KRT10, KRT13, KRT14, KRT15, KRT16, KRT17,KRT18, KRT19, KRT2, KRT20, KRT24, KRT25, KRT27, KRT28, KRT3, KRT4, KRT5,KRT6A, KRT6B, KRT6C, KRT7, KRT75, KRT76, KRT77, KRT79, KRT8, KRT9,LAMA5, LAMP1, LDHA, LDHB, LFNG, LGALS3, LGALS3BP, LGALS4, LIMA1, LIN7A,LIN7C, LOC100128936, LOC100130553, LOC100133382, LOC100133739,LOC284889, LOC388524, LOC388720, LOC442497, LOC653269, LRP4, LRPPRC,LRSAM1, LSR, LYZ, MAN1A1, MAP4K4, MARCKS, MARCKSL1, METRNL, MFGE8, MICA,MIF, MINK1, MITD1, MMP7, MOBKL1A, MSN, MTCH2, MUC13, MYADM, MYH10,MYH11, MYH14, MYH9, MYL6, MYL6B, MYO1C, MYO1D, NARS, NCALD, NCSTN,NEDD4, NEDD4L, NME1, NME2, NOTCH1, NQO1, NRAS, P4HB, PCBP1, PCNA, PCSK9,PDCD6, PDCD6IP, PDIA3, PDXK, PEBP1, PFN1, PGK1, PHB, PHB2, PKM2, PLEC1,PLEKHB2, PLSCR3, PLXNA1, PLXNB2, PPIA, PPIB, PPP2R1A, PRDX1, PRDX2,PRDX3, PRDX5, PRDX6, PRKAR2A, PRKDC, PRSS23, PSMA2, PSMC6, PSMD11,PSMD3, PSME3, PTGFRN, PTPRF, PYGB, QPCT, QSOX1, RAB10, RAB11A, RAB11B,RAB13, RAB14, RAB15, RAB1A, RAB1B, RAB2A, RAB33B, RAB35, RAB43, RAB4B,RAB5A, RAB5B, RAB5C, RAB6A, RAB6B, RAB7A, RAB8A, RAB8B, RAC1, RAC3,RALA, RALB, RAN, RANP1, RAP1A, RAP1B, RAP2A, RAP2B, RAP2C, RDX, REG4,RHOA, RHOC, RHOG, ROCK2, RP11-631M21.2, RPL10A, RPL12, RPL6, RPL8,RPLP0, RPLP0-like, RPLP1, RPLP2, RPN1, RPS13, RPS14, RPS15A, RPS16,RPS18, RPS20, RPS21, RPS27A, RPS3, RPS4X, RPS4Y1, RPS4Y2, RPS7, RPS8,RPSA, RPSAP15, RRAS, RRAS2, RUVBL1, RUVBL2, S100A10, S100A11, S100A14,S100A16, S100A6, S100P, SDC1, SDC4, SDCBP, SDCBP2, SERINC1, SERINC5,SERPINA1, SERPINF1, SETD4, SFN, SLC12A2, SLC12A7, SLC16A1, SLC1A5,SLC25A4, SLC25A5, SLC25A6, SLC29A1, SLC2A1, SLC3A2, SLC44A1, SLC7A5,SLC9A3R1, SMPDL3B, SNAP23, SND1, SOD1, SORT1, SPTAN1, SPTBN1, SSBP1,SSR4, TACSTD1, TAGLN2, TBCA, TCEB1, TCP1, TF, TFRC, THBS1, TJP2, TKT,TMED2, TNFSF10, TNIK, TNKS1BP1, TNPO3, TOLLIP, TOMM22, TPI1, TPM1,TRAP1, TSG101, TSPAN1, TSPAN14, TSPAN15, TSPAN6, TSPAN8, TSTA3, TTYH3,TUBA1A, TUBA1B, TUBA1C, TUBA3C, TUBA3D, TUBA3E, TUBA4A, TUBA4B, TUBA8,TUBB, TUBB2A, TUBB2B, TUBB2C, TUBB3, TUBB4, TUBB4Q, TUBB6, TUFM, TXN,UBA1, UBA52, UBB, UBC, UBE2N, UBE2V2, UGDH, UQCRC2, VAMP1, VAMP3, VAMP8,VCP, VIL1, VPS25, VPS28, VPS35, VPS36, VPS37B, VPS37C, WDR1, YWHAB,YWHAE, YWHAG, YWHAH, YWHAQ, YWHAZ

The colon cancer can be characterized using one or more processesdisclosed herein with at least 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,or 70% sensitivity. The colon cancer can be characterized with at least71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, or 87%sensitivity. For example, the colon cancer can be characterized with atleast 87.1, 87.2, 87.3, 87.4, 87.5, 87.6, 87.7, 87.8, 87.9, 88.0, or 89%sensitivity, such as with at least 90% sensitivity, such as at least 91,92, 93, 94, 95, 96, 97, 98, 99 or 100% sensitivity.

The colon cancer of a subject can also be characterized with at least70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,88, 89, 90, 91, 92, 93, 94, 95, 96, or 97% specificity, such as with atleast 97.1, 97.2, 97.3, 97.4, 97.5, 97.6, 97.7, 97.8, 97.8, 97.9, 98.0,98.1, 98.2, 98.3, 98.4, 98.5, 98.6, 98.7, 98.8, 98.9, 99.0, 99.1, 99.2,99.3, 99.4, 99.5, 99.6, 99.7, 99.8, 99.9 or 100% specificity.

The colon cancer can also be characterized with at least 70% sensitivityand at least 80, 90, 95, 99, or 100% specificity; at least 80%sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; atleast 85% sensitivity and at least 80, 85, 90, 95, 99, or 100%specificity; at least 86% sensitivity and at least 80, 85, 90, 95, 99,or 100% specificity; at least 87% sensitivity and at least 80, 85, 90,95, 99, or 100% specificity; at least 88% sensitivity and at least 80,85, 90, 95, 99, or 100% specificity; at least 89% sensitivity and atleast 80, 85, 90, 95, 99, or 100% specificity; at least 90% sensitivityand at least 80, 85, 90, 95, 99, or 100% specificity; at least 95%sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; atleast 99% sensitivity and at least 80, 85, 90, 95, 99, or 100%specificity; or at least 100% sensitivity and at least 80, 85, 90, 95,99, or 100% specificity.

Furthermore, the confidence level for determining the specificity,sensitivity, and/or other statistical performance measures may be withat least 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85,86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99% confidence.

Ovarian Cancer

A biosignature for characterizing ovarian cancer can comprise an antigenassociated with ovarian cancer (for example, as shown in FIG. 1), andone or more additional biomarkers, such as shown in FIG. 4. In oneembodiment, a biosignature for ovarian cancer can comprise one or moreantigens associated with ovarian cancer, such as, but not limited to,CD24, CA125, VEGF1, VEGFR2, HER2, MISIIR, or any combination thereof.The biosignature for ovarian cancer can comprise one or more of theaforementioned antigens and one or more additional biomarker, such as,but not limited to miRNA, mRNA, DNA, or any combination thereof. Thebiosignature for ovarian cancer can comprise one or more antigensassociated with ovarian cancer, such as, but not limited to, CD24,CA125, VEGF1, VEGFR2, HER2, MISIIR, or any combination thereof, with oneor more miRNA biomarkers, such as, but not limited to, miR-200a,miR-141, miR-200c, miR-200b, miR-21, miR-141, miR-200a, miR-200b,miR-200c, miR-203, miR-205, miR-214, miR-215, miR-199a, miR-140,miR-145, miR-125b-1, or any combination thereof.

A biosignature for ovarian cancer can comprise one or more antigensassociated with ovarian cancer, such as, but not limited to, CD24,CA125, VEGF1, VEGFR2, HER2, MISIIR, or any combination thereof, with oneor more miRNA biomarkers (such as the aforementioned miRNA), mRNAs (suchas, but not limited to, ERCC1, ER, TOPO1, TOP2A, AR, PTEN, HER2/neu,EGFR), mutations (including, but not limited to, those relating to KRASand/or B-Raf) or any combination thereof.

A vesicle can be isolated, assayed or both, for one or more miRNA andone or more antigens associated with ovarian cancer to provide adiagnostic, prognostic or theranostic profile. Alternatively, thevesicle can be directly assayed from a sample, such that the vesicle isnot purified or concentrated prior to assaying for one or more miRNA orantigens associated with ovarian cancer.

Organ Transplant Rejection and Autoimmune Conditions

A vesicle can also be used for determining phenotypes such as organdistress and/or organ transplant rejection. As used herein organtransplant includes partial organ or tissue transplant. The presence,absence or levels of one or more biomarkers present in a vesicle can beassessed to monitor organ rejection or success. The level or amount ofvesicles in the sample can also be used to assess organ rejection orsuccess. The assessment can be determined with at least 70, 71, 72, 73,74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,92, 93, 94, 95, 96, 97, 98, or 99% specificity, sensitivity, or both.For example, the assessment can be determined with at least 97.5, 97.6,97.7, 97.8, 97.8, 97.9, 98.0, 98.1, 98.2, 98.3, 98.4, 98.5, 98.6, 98.7,98.8, 98.9, 99.0, 99.1, 998.2, 99.3, 99.4, 99.5, 99.6, 99.7, 99.8, 99.9%sensitivity, specificity, or both

The vesicle can be purified or concentrated prior to analysis.Alternatively, the level, or amount, of vesicles can be directly assayedfrom a sample, without prior purification or concentration. The vesiclecan be quantitated, su. For example, a cell or tissue-specific vesiclecan be isolated using one or more binding agents specific for aparticular organ. The cell-of-origin specific vesicle can be assessedfor one or more molecular features, such as one or more biomarkersassociated with organ distress or organ transplant rejection. Thepresence, absence or levels of one or more biomarkers present, can beassessed to monitor organ rejection or success.

One or more vesicles can be analyzed for the assessment, detection ordiagnosis of the rejection of a tissue or organ transplant by a subject.The tissue or organ transplant rejection can be hyperacute, acute, orchronic rejection. The vesicle can also be analyzed for the assessment,detection or diagnosis of graft versus host disease in a subject. Thesubject can be the recipient of an autogenic, allogenic or xenogenictissue or organ transplant.

The vesicle can also be analyzed to detect the rejection of a tissue ororgan transplant. The vesicle may be produced by the tissue or organtransplant. Such tissues or organs include, but are not limited to, aheart, lung, pancreas, kidney, eye, cornea, muscle, bone marrow, skin,cartilage, bone, appendages, hair, face, tendon, stomach, intestine,vein, artery, differentiated cells, partially differentiated cells orstem cells.

The vesicle can comprise at least one biomarker which is used to assess,diagnose or determine the probability or occurrence of rejection of atissue or organ transplant by a subject. A biomarker can also be used toassess, diagnose or detect graft versus host disease in a subject. Thebiomarker can be a protein, a polysaccharide, a fatty acid or a nucleicacid (such as DNA or RNA). The biomarker can be associated with therejection of a specific tissue or organ or systemic organ failure. Morethan one biomarker can be analyzed, for example, one or more proteinsmarker can be analyzed in combination with one or more nucleic acidmarkers. The biomarker may be an intracellular or extracellular marker.

The vesicle can also be analyzed for at least one marker for theassessment, detection or diagnosis of cell apoptosis or necrosisassociated with, or the causation of, rejection of a tissue or organtransplant by a subject.

The presence of a biomarker can be indicative of the rejection of atissue or an organ by a subject, wherein the biomarker includes, but isnot limited to, CD40, CD40 ligand, N-acetylmuramoyl-L-alanine amidaseprecursor, adiponectin, AMBP protein precursor, C4b-binding proteina-chain precursor, ceruloplasmin precursor, complement C3 precursor,complement component C9 precursor, complement factor D precursor,alpha1-B-glycoprotein, beta2-glycoprotein I precursor, heparin cofactorII precursor, Immunoglobulin mu chain C region protein, Leucine-richalpha2-glycoprotein precursor, pigment epithelium-derived factorprecursor, plasma retinol-binding protein precursor, translationinitiation factor 3 subunit 10, ribosomal protein L7, beta-transducin,1-TRAF, or lysyl-tRNA synthetase.

Rejection of a kidney by a subject can also be detected by analyzingvesicles for the presence of beta-transducin. Rejection of transplantedtissue can also be detected by isolating a cell-of-origin specificvesicles from CD40-expressing cells and detecting for the increase ofBcl-2 or TNFalpha.

Rejection of a liver transplant by a subject can be detected byanalyzing the vesicles for the presence of an F1 antigen marker. The F1antigen is, without being bound to theory, specific to liver to and canbe used to detect an increase in liver cell-of-origin specific vesicles.This increase can be used as an early indication of organdistress/rejection.

Bronchiolitis obliterans due to bone marrow and/or lung transplantationor other causes, or graft atherosclerosis/graft phlebosclerosis can alsobe diagnosed by the analysis of a vesicle.

A vesicle can also be analyzed for the detection, diagnosis orassessment of an autoimmune or other immunological reaction-relatedphenotype in a subject. Examples of such a disorder include, but are notlimited to, systemic lupus erythematosus (SLE), discoid lupus, lupusnephritis, sarcoidosis, inflammatory arthritis, including juvenilearthritis, rheumatoid arthritis, psoriatic arthritis, Reiter's syndrome,ankylosing spondylitis, and gouty arthritis, multiple sclerosis, hyperIgE syndrome, polyarteritis nodosa, primary biliary cirrhosis,inflammatory bowel disease, Crohn's disease, celiac's disease(gluten-sensitive enteropathy), autoimmune hepatitis, pernicious anemia,autoimmune hemolytic anemia, psoriasis, scleroderma, myasthenia gravis,autoimmune thrombocytopenic purpura, autoimmune thyroiditis, Grave'sdisease, Hasimoto's thyroiditis, immune complex disease, chronic fatigueimmune dysfunction syndrome (CFIDS), polymyositis and dermatomyositis,cryoglobulinemia, thrombolysis, cardiomyopathy, pemphigus vulgaris,pulmonary interstitial fibrosis, asthma, Churg-Strauss syndrome(allergic granulomatosis), atopic dermatitis, allergic and irritantcontact dermatitis, urtecaria, IgE-mediated allergy, atherosclerosis,vasculitis, idiopathic inflammatory myopathies, hemolytic disease,Alzheimer's disease, chronic inflammatory demyelinating polyneuropathyand AIDs.

One or more biomarkers from the vesicles can be used to assess, diagnoseor determine the probability of the occurrence of an autoimmune or otherimmunological reaction-related disorder in a subject. The biomarker canbe a protein, a polysaccharide, a fatty acid or a nucleic acid (such asDNA or RNA). The biomarker can be associated with a specific autoimmunedisorder, a systemic autoimmune disorder, or other immunologicalreaction-related disorder. More than one biomarker can be analyzed. Forexample one or more protein markers can be analyzed in combination withone or more nucleic acid markers. The biomarker can be an intracellularor extracellular marker. The biomarker can also be used to detect,diagnose or assess inflammation.

Analysis of vesicles from subjects can be used identify subjects withinflammation associated with asthma, sarcoidosis, emphysema, cysticfibrosis, idiopathic pulmonary fibrosis, chronic bronchitis, allergicrhinitis and allergic diseases of the lung such as hypersensitivitypneumonitis, eosinophilic pneumonia, as well as pulmonary fibrosisresulting from collagen, vascular, and autoimmune diseases such asrheumatoid arthritis.

Theranosis

As disclosed herein, methods are disclosed for characterizing aphenotype for a subject by assessing one or more biomarkers, includingvesicle biomarkers and/or circulating biomarkers. The biomarkers can beassessed using methods for multiplexed analysis of vesicle biomarkersdisclosed herein. Characterizing a phenotype can include providing atheranosis for a subject, such as determining if a subject is predictedto respond to a treatment or is predicted to be non-responsive to atreatment. A subject that responds to a treatment can be termed aresponder whereas a subject that does not respond can be termed anon-responder. A subject suffering from a condition can be considered tobe a responder for a treatment based on, but not limited to, animprovement of one or more symptoms of the condition; a decrease in oneor more side effects of an existing treatment; an increased improvement,or rate of improvement, in one or more symptoms as compared to aprevious or other treatment; or prolonged survival as compared towithout treatment or a previous or other treatment. For example, asubject suffering from a condition can be considered to be a responderto a treatment based on the beneficial or desired clinical resultsincluding, but are not limited to, alleviation or amelioration of one ormore symptoms, diminishment of extent of disease, stabilized (i.e., notworsening) state of disease, preventing spread of disease, delay orslowing of disease progression, amelioration or palliation of thedisease state, and remission (whether partial or total), whetherdetectable or undetectable. Treatment also includes prolonging survivalas compared to expected survival if not receiving treatment or ifreceiving a different treatment.

The systems and methods disclosed herein can be used to select acandidate treatment for a subject in need thereof. Selection of atherapy can be based on one or more characteristics of a vesicle, suchas the biosignature of a vesicle, the amount of vesicles, or both.Vesicle typing or profiling, such as the identification of thebiosignature of a vesicle, the amount of vesicles, or both, can be usedto identify one or more candidate therapeutic agents for an individualsuffering from a condition. For example, vesicle profiling can be usedto determine if a subject is a non-responder or responder to aparticular therapeutic, such as a cancer therapeutic if the subject issuffering from a cancer.

Vesicle profiling can be used to provide a diagnosis or prognosis for asubject, and a therapy can be selected based on the diagnosis orprognosis. Alternatively, therapy selection can be directly based on asubject's vesicle profile. Furthermore, a subject's vesicle profile canbe used to follow the evolution of a disease, to evaluate the efficacyof a medication, adapt an existing treatment for a subject sufferingfrom a disease or condition, or select a new treatment for a subjectsuffering from a disease or condition.

A subject's response to a treatment can be assessed using biomarkers,including vesicles, microRNA, and other circulating biomarkers. In oneembodiment, a subject is determined, classified, or identified as anon-responder or responder based on the subject's vesicle profileassessed prior to any treatment. During pretreatment, a subject can beclassifed as a non-responder or responder, thereby reducing unnecessarytreatment options, and avoidance of possible side effects fromineffective therapeutics. Furthermore, the subject can be identified asa responder to a particular treatment, and thus vesicle profiling can beused to prolong survival of a subject, improve the subject's symptoms orcondition, or both, by providing personalized treatment options. Thus, asubject suffering from a condition can have a biosignature generatedfrom vesicles and other circulating biomarkers using one or more systemsand methods disclosed herein, and the profile can then be used todetermine whether a subject is a likely non-responder or responder to aparticular treatment for the condition. Based on use of the biosignatureto predict whether the subject is a non-responder or responder to theinitially contemplated treatment, a particular treatment contemplatedfor treating the subject's condition can be selected for the subject, oranother potentially more optimal treatment can be selected.

In one embodiment, a subject suffering from a condition is currentlybeing treated with a therapeutic. A sample can be obtained from thesubject before treatment and at one or more timepoints during treatment.A biosignature including vesicles or other biomarkers from the samplescan be assessed and used to determine the subject's response to thedrug, such as based on a change in the biosignature over time. If thesubject is not responding to the treatment, e.g., the biosignature doesnot indicate that the patient is responding, the subject can beclassified as being non-responsive to the treatment, or a non-responder.Similarly, one or more biomarkers associated with a worsening conditionmay be detected such that the biosignature is indicative of patient'sfailure to respond favorably to the treatment. In another example, oneor more biomarkers associated with the condition remain the same despitetreatment, indicating that the condition is not improving. Thus, basedon the biosignature, a treatment regimen for the subject can be changedor adapted, including selection of a different therapeutic.

Alternatively, the subject can be determined to be responding to thetreatment, and the subject can be classified as being responsive to thetreatment, or a responder. For example, one or more biomarkersassociated with an improvement in the condition or disorder may bedetected. In another example, one or more biomarkers associated with thecondition changes, thus indicating an improvement. Thus, the existingtreatment can be continued. In another embodiment, even when there is anindiciation of improvement, the existing treatment may be adapted orchanged if the biosignature indicates that another line of treatment maybe more effective. The existing treatment may be combined with anothertherapeutic, the dosage of the current therapeutic may be increased, ora different candidate treatment or therapeutic may be selected. Criteriafor selecting the different candidate treatment can depend on thesetting. In one embodiment, the candidate treatment may have been knownto be effective for subjects with success on the existing treatment. Inanother embodiment, the candidate treatment may have been known to beeffective for other subjects with a similar biosignature.

In some embodiments, the subject is undergoing a second, third or moreline of treatment, such as cancer treatment. A biosignature according tothe invention can be determined for the subject prior to a second, thirdor more line of treatment, to determine whether a subject would be aresponder or non-resonder to the second, third or more line oftreatment. In another embodiment, a biosignature is determined for thesubject during the second, third or more line of treatment, to determineif the subject is responding to the second, third or more line oftreatment.

The methods and systems described herein for assessing one or morevesicles can be used to determine if a subject suffering from acondition is responsive to a treatment, and thus can be used to select atreatment that improves one or more symptoms of the condition; decreasesone or more side effects of an existing treatment; increases theimprovement, or rate of improvement, in one or more symptoms as comparedto a previous or other treatment; or prolongs survival as compared towithout treatment or a previous or other treatment. Thus, the methodsdescribed herein can be used to prolong survival of a subject byproviding personalized treatment options, and/or may reduce unnecessarytreatment options and unnecessary side effects for a subject.

The prolonged survival can be an increased progression-free survival(PFS), which denotes the chances of staying free of disease progressionfor an individual or a group of individuals suffering from a disease,e.g., a cancer, after initiating a course of treatment. It can refer tothe percentage of individuals in the group whose disease is likely toremain stable (e.g., not show signs of progression) after a specifiedduration of time. Progression-free survival rates are an indication ofthe effectiveness of a particular treatment. In other embodiments, theprolonged survival is disease-free survival (DFS), which denotes thechances of staying free of disease after initiating a particulartreatment for an individual or a group of individuals suffering from acancer. It can refer to the percentage of individuals in the group whoare likely to be free of disease after a specified duration of time.Disease-free survival rates are an indication of the effectiveness of aparticular treatment. Two treatment strategies can be compared on thebasis of the disease-free survival that is achieved in similar groups ofpatients. Disease-free survival is often used with the term overallsurvival when cancer survival is described.

The candidate treatment selected by vesicle profiling as describedherein can be compared to a non-vesicle profiling selected treatment bycomparing the progression free survival (PFS) using therapy selected byvesicle profiling (period B) with PFS for the most recent therapy onwhich the subject has just progressed (period A). In one setting, aPFSB/PFSA ratio ≧1.3 is used to indicate that the vesicle profilingselected therapy provides benefit for subject (see for example, RobertTemple, Clinical measurement in drug evaluation. Edited by Wu Ninganoand G. T. Thicker John Wiley and Sons Ltd. 1995; Von Hoff D. D. Clin CanRes. 4: 1079, 1999: Dhani et al. Clin Cancer Res. 15: 118-123, 2009).

Other methods of comparing the treatment selected by vesicle profilingcan be compared to a non-vesicle profiling selected treatment bydetermine response rate (RECIST) and percent of subjects withoutprogression or death at 4 months. The term “about” as used in thecontext of a numerical value for PFS means a variation of +/−ten percent(10%) relative to the numerical value. The PFS from a treatment selectedby vesicle profiling can be extended by at least 10%, 15%, 20%, 30%,40%, 50%, 60%, 70%, 80%, or at least 90% as compared to a non-vesicleprofiling selected treatment. In some embodiments, the PFS from atreatment selected by vesicle profiling can be extended by at least100%, 150%, 200%, 300%, 400%, 500%, 600%, 700%, 800%, 900%, or at leastabout 1000% as compared to a non-vesicle profiling selected treatment.In yet other embodiments, the PFS ratio (PFS on vesicle profilingselected therapy or new treatment/PFS on prior therapy or treatment) isat least about 1.3. In yet other embodiments, the PFS ratio is at leastabout 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, or 2.0. In yet otherembodiments, the PFS ratio is at least about 3, 4, 5, 6, 7, 8, 9 or 10.

Similarly, the DFS can be compared in subjects whose treatment isselected with or without determining a biosignature according to theinvention. The DFS from a treatment selected by vesicle profiling can beextended by at least 10%, 15%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or atleast 90% as compared to a non-vesicle profiling selected treatment. Insome embodiments, the DFS from a treatment selected by vesicle profilingcan be extended by at least 100%, 150%, 200%, 300%, 400%, 500%, 600%,700%, 800%, 900%, or at least about 1000% as compared to a non-vesicleprofiling selected treatment. In yet other embodiments, the DFS ratio(DFS on vesicle profiling selected therapy or new treatment/DFS on priortherapy or treatment) is at least about 1.3. In yet other embodiments,the DFS ratio is at least about 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8,1.9, or 2.0. In yet other embodiments, the DFS ratio is at least about3, 4, 5, 6, 7, 8, 9 or 10.

In some embodiments, the candidate treatment selected by microvescileprofiling does not increase the PFS ratio or the DFS ratio in thesubject; nevertheless vesicle profiling provides subject benefit. Forexample, in some embodiments no known treatment is available for thesubject. In such cases, vesicle profiling provides a method to identifya candidate treatment where none is currently identified. The vesicleprofiling may extend PFS, DFS or lifespan by at least 1 week, 2 weeks, 3weeks, 4 weeks, 1 month, 5 weeks, 6 weeks, 7 weeks, 8 weeks, 2 months, 9weeks, 10 weeks, 11 weeks, 12 weeks, 3 months, 4 months, 5 months, 6months, 7 months, 8 months, 9 months, 10 months, 11 months, 12 months,13 months, 14 months, 15 months, 16 months, 17 months, 18 months, 19months, 20 months, 21 months, 22 months, 23 months, 24 months or 2years. The vesicle profiling may extend PFS, DFS or lifespan by at least2½ years, 3 years, 4 years, 5 years, or more. In some embodiments, themethods of the invention improve outcome so that subject is inremission.

The effectiveness of a treatment can be monitored by other measures. Acomplete response (CR) comprises a complete disappearance of thedisease: no disease is evident on examination, scans or other tests. Apartial response (PR) refers to some disease remaining in the body, butthere has been a decrease in size or number of the lesions by 30% ormore. Stable disease (SD) refers to a disease that has remainedrelatively unchanged in size and number of lesions. Generally, less thana 50% decrease or a slight increase in size would be described as stabledisease. Progressive disease (PD) means that the disease has increasedin size or number on treatment. In some embodiments, vesicle profilingaccording to the invention results in a complete response or partialresponse. In some embodiments, the methods of the invention result instable disease. In some embodiments, the invention is able to achievestable disease where non-vesicle profiling results in progressivedisease.

The theranosis based on a biosignature of the invention can be for aphenotype including without limitation those listed herein.Characterizing a phenotype includes determining a theranosis for asubject, such as predicting whether a subject is likely to respond to atreatment (“responder”) or be non-responsive to a treatment(“non-responder”). As used herein, identifying a subject as a“responder” to a treatment or as a “non-responder” to the treatmentcomprises identifying the subject as either likely to respond to thetreatment or likely to not respond to the treatment, respectively, anddoes not require determining a definitive prediction of the subject'sresponse. One or more vesicles, or populations of vesicles, obtainedfrom subject are used to determine if a subject is a non-responder orresponder to a particular therapeutic, by assessing biomarkers disclosedherein, e.g., those listed in Table 7. Detection of a high or lowexpression level of a biomarker, or a mutation of a biomarker, can beused to select a candidate treatment, such as a pharmaceuticalintervention, for a subject with a condtion. Table 7 containsillustrative conditions and pharmaceutical interventions for thoseconditions. The table lists biomarkers that affect the efficacy of theintervention. The biomarkers can be assessed using the methods of theinvention, e.g., as circulating biomarkers or in association with avesicle.

TABLE 7 Examples of Biomarkers and Pharmaceutical Intervention for aCondition Condition Pharmaceutial intervention Biomarker PeripheralArterial Disease Atorvastatin C-reactive protein(CRP) Simvastatin serumAmylyoid A (SAA) Rosuvastatin interleukin-6 Pravastatin intracellularadhesion molecule Fluvastatin (ICAM) Lovastatin vascular adhesionmolecule (VCAM) CD40L fibrinogen fibrin D-dimer fibrinopeptide A vonWillibrand factor tissue plasminogen activator antigen (t-PA) factor VIIprothrombin fragment 1 oxidized low density lipoprotein (oxLDL)lipoprotein A Non-Small Cell Lung Cancer Erlotinib EGFR Carboplatinexcision repair cross- Paclitaxel complementation group 1 (ERCC1)Gefitinib p53 Ras p27 class III beta tubulin breast cancer gene 1(BRCA1) breast cancer gene 1 (BRCA2) ribonucleotide reductase messenger1 (RRM1) Colorectal Cancer Panitumumab K-ras Cetuximab Breast CancerTrastuzumab HER2 Anthracyclines toposiomerase IIalpha Taxane estrogenreceptor Methotrexate progesterone receptor fluorouracil Alzheimer'sDisease Donepezil beta-amyloid protein Galantamine amyloid precursorprotein (APP) Memantine APP670/671 Rivastigmine APP693 Tacrine APP692APP715 APP716 APP717 APP723 presenilin 1 presenilin 2 cerebrospinalfluid amyloid beta protein 42 (CSF-Abeta42) cerebrospinal fluid amyloidbeta protein 40 (CSF-Abeta40) F2 isoprostane 4-hydroxynonenal F4neuroprostane acrolein Arrhythmia Disopyramide SERCA Flecainide AAPLidocaine Connexin 40 Mexiletine Connexin 43 Moricizine ATP-sensitivepotassium channel Procainamide Kv1.5 channel Propafenoneacetylcholine-activated posassium Quinidine channel Tocainide AcebutololAtenolol Betaxolol Bisoprolol Carvedilol Esmolol Metoprolol NadololPropranolol Sotalol Timolol Amiodarone Azimilide Bepridil DofetilideIbutilide Tedisamil Diltiazem Verapamil Azimilide Dronedarone AmiodaronePM101 ATI-2042 Tedisamil Nifekalant Ambasilide Ersentilide TrecetilideAlmokalant D-sotalol BRL-32872 HMR1556 L768673 Vernakalant AZD70009AVE0118 S9947 NIP-141/142 XEN-D0101/2 Ranolazine Pilsicainide JTV519Rotigaptide GAP-134 Rheumatoid arthritis Methotrexate 677CC/1298AA MTHFRinfliximab 677CT/1298AC MTHFR adalimumab 677CT MTHFR etanercept G80AARFC-1 sulfasalazine 3435TT MDR1 (ABCB1) 3435TT ABCB1 AMPD1/ATIC/ITPAIL1-RN3 HLA-DRB103 CRP HLA-D4 HLA DRB-1 anti-citrulline epitopecontaining peptides anti-A1/RA33 Erythrocyte sedimentation rate (ESR)C-reactive protein (CRP) SAA (serum amyloid-associated protein)rheumatoid factor IL-1 TNF IL-6 IL-8 IL-1Ra Hyaluronic acid AggrecanGlc-Gal-PYD osteoprotegerin RNAKL carilage oligomeric matrix protein(COMP) calprotectin Arterial Fibrillation warfarin F1.2 aspirin TATanticoagulants FPA heparin beta-throboglobulin ximelagatran plateletfactor 4 soluble P-selectin IL-6 CRP HIV Infection Zidovudine HIV p24antigen Didanosine TNF-alpha Zalcitabine TNFR-II Stavudine CD3Lamivudine CD14 Saquinavir CD25 Ritonavir CD27 Indinavir Fas NeviraneFasL Nelfinavir beta2 microglobulin Delavirdine neopterin Stavudine HIVRNA Efavirenz HLA-B *5701 Etravirine Enfuvirtide Darunavir AbacavirAmprenavir Lonavir/Ritonavirc Tenofovir Tipranavir CardiovascularDisease lisinopril ACE inhibitor candesartan angiotensin enalapril

Cancer

Vesicle biosignatures can be used in the theranosis of a cancer, such asidentifying whether a subject suffering from cancer is a likelyresponder or non-responder to a particular cancer treatment. The subjectmethods can be used to theranose cancers including those listed herein,e.g., in the “Phenotype” section above. These include without limitationlung cancer, non-small cell lung cancerm small cell lung cancer(including small cell carcinoma (oat cell cancer), mixed smallcell/large cell carcinoma, and combined small cell carcinoma), coloncancer, breast cancer, prostate cancer, liver cancer, pancreatic cancer,brain cancer, kidney cancer, ovarian cancer, stomach cancer, melanoma,bone cancer, gastric cancer, breast cancer, glioma, gliobastoma,hepatocellular carcinoma, papillary renal carcinoma, head and necksquamous cell carcinoma, leukemia, lymphoma, myeloma, or other solidtumors.

Cancer: Biosignatures

A biosignature can be determined to provide a theranosis for a subject.The biosignature of a vesicle can comprise one or more biomarkers suchas, but not limited to, any one or more biomarkers as described herein,such as, but not limited to, those listed in FIGS. 1, 3, 6, 7, 9-12,14-22, 25-33, 50-51, 53-54, 59, and 60.

The invention provides numerous methods of identifying a biosignaturefor characterizing a cancer. Further provided herein are biomarkers thatare assessed to identify the biosignature. In one embodiment, abiosignature for prostate cancer comprises one or more of the followingbiomarkers: EpCam, CD9, PCSA, CD63, CD81, PSMA, B7H3, PSCA, ICAM, STEAP,and EGFR. In another embodiment, a biosignature for classifying aprostate cancer as being castration-resistant comprises EpCam+, CK+,CD45− vesicles. In another embodiment, a vesicle biosignature fortheranosing small cell lung cancer comprises miR-451, miR-92a-2*,miR-147, and/or miR-574-5p. In yet another embodiment, a biosignaturefor the theranosis of colorectal cancer comprises one or more miRsselected from the group consisting of: miR-548c-5p, miR-362-3p,miR-422a, miR-597, miR-429, miR-200a, and miR-200b. The biosignature fortheranosing a cancer can comprise one or more of CA IX, CMET, VEGFR2,VEGF, Vimentin, CD44v6, Ckit, Axl, RET (ret proto-oncogene), E Cadherin,and VE Cadherin.

Cancer: Standard of Care

A biosignature of circulating biomarkers, including markers associatedwith vesicle, in a sample from a subject suffering from a cancer can beused select a candidate treatment for the subject. The biosignature canbe determined according to the methods of the invention presentedherein. In some embodiments, the candidate treatment comprises astandard of care for the cancer. The biosignature can be used todetermine if a subject is a non-responder or responder to a particulartreatment or standard of care. The treatment can be a cancer treatmentsuch as radiation, surgery, chemotherapy or a combination thereof. Thecancer treatment can be a therapeutic such as anti-cancer agents andchemotherapeutic regimens. Cancer treatments for use with the methods ofthe invention include without limitation those listed in Table 8:

TABLE 8 Cancer Treatments Treatment or Agent Cancer therapies Radiation,Surgery, Chemotherapy, Biologic therapy, Neo-adjuvant therapy, Adjuvanttherapy, Palliative therapy, Watchful waiting Anti-cancer agents13-cis-Retinoic Acid, 2-CdA, 2-Chlorodeoxyadenosine, 5-Azacitidine,5-Fluorouracil, (chemotherapies and 5-FU, 6-Mercaptopurine, 6-MP, 6-TG,6-Thioguanine, Abraxane, Accutane ®, biologics) Actinomycin-D,Adriamycin ®, Adrucil ®, Afinitor ®, Agrylin ®, Ala-Cort ®, Aldesleukin,Alemtuzumab, ALIMTA, Alitretinoin, Alkaban-AQ ®, Alkeran ®, All-transretinoic Acid, Alpha Interferon, Altretamine, Amethopterin,Amifostine, Aminoglutethimide, Anagrelide, Anandron ®, Anastrozole,Arabinosylcytosine, Ara-C, Aranesp ®, Aredia ®, Arimidex ®, Aromasin ®,Arranon ®, Arsenic Trioxide, Asparaginase, ATRA, Avastin ®, Azacitidine,BCG, BCNU, Bendamustine, Bevacizumab, Bexarotene, BEXXAR ®,Bicalutamide, BiCNU, Blenoxane ®, Bleomycin, Bortezomib, Busulfan,Busulfex ®, C225, Calcium Leucovorin, Campath ®, Camptosar ®,Camptothecin-11, Capecitabine, Carac ™, Carboplatin, Carmustine,Carmustine Wafer, Casodex ®, CC-5013, CCI-779, CCNU, CDDP, CeeNU,Cerubidine ®, Cetuximab, Chlorambucil, Cisplatin, Citrovorum Factor,Cladribine, Cortisone, Cosmegen ®, CPT-11, Cyclophosphamide, Cytadren ®,Cytarabine, Cytarabine Liposomal, Cytosar-U ®, Cytoxan ®, Dacarbazine,Dacogen, Dactinomycin, Darbepoetin Alfa, Dasatinib, DaunomycinDaunorubicin, Daunorubicin Hydrochloride, Daunorubicin Liposomal,DaunoXome ®, Decadron, Decitabine, Delta-Cortef ®, Deltasone ®,Denileukin, Diftitox, DepoCyt ™, Dexamethasone, Dexamethasone AcetateDexamethasone Sodium Phosphate, Dexasone, Dexrazoxane, DHAD, DIC, DiodexDocetaxel, Doxil ®, Doxorubicin, Doxorubicin Liposomal, Droxia ™, DTIC,DTIC- Dome ®, Duralone ®, Efudex ®, Eligard ™, Ellence ™, Eloxatin ™,Elspar ®, Emcyt ®, Epirubicin, Epoetin Alfa, Erbitux, Erlotinib, ErwiniaL-asparaginase, Estramustine, Ethyol Etopophos ®, Etoposide, EtoposidePhosphate, Eulexin ®, Everolimus, Evista ®, Exemestane, Fareston ®,Faslodex ®, Femara ®, Filgrastim, Floxuridine, Fludara ®, Fludarabine,Fluoroplex ®, Fluorouracil, Fluorouracil (cream), Fluoxymesterone,Flutamide, Folinic Acid, FUDR ®, Fulvestrant, G-CSF, Gefitinib,Gemcitabine, Gemtuzumab ozogamicin, Gemzar, Gleevec ™, Gliadel ® Wafer,GM-CSF, Goserelin, Granulocyte - Colony Stimulating Factor, GranulocyteMacrophage Colony Stimulating Factor, Halotestin ®, Herceptin ®,Hexadrol, Hexalen ®, Hexamethylmelamine, HMM, Hycamtin ®, Hydrea ®,Hydrocort Acetate ®, Hydrocortisone, Hydrocortisone Sodium Phosphate,Hydrocortisone Sodium Succinate, Hydrocortone Phosphate, Hydroxyurea,Ibritumomab, Ibritumomab, Tiuxetan, Idamycin ®, Idarubicin, Ifex ®,IFN-alpha, Ifosfamide, IL-11, IL-2, Imatinib mesylate, ImidazoleCarboxamide, Interferon alfa, Interferon Alfa-2b (PEG Conjugate),Interleukin-2, Interleukin-11, Intron A ® (interferon alfa-2b),Iressa ®, Irinotecan, Isotretinoin, Ixabepilone, Ixempra ™, Kidrolase(t), Lanacort ®, Lapatinib, L-asparaginase, LCR, Lenalidomide,Letrozole, Leucovorin, Leukeran, Leukine ™, Leuprolide, Leurocristine,Leustatin ™, Liposomal Ara-C Liquid Pred ®, Lomustine, L-PAM,L-Sarcolysin, Lupron ®, Lupron Depot ®, Matulane ®, Maxidex,Mechlorethamine, Mechlorethamine Hydrochloride, Medralone ®, Medrol ®,Megace ®, Megestrol, Megestrol Acetate, Melphalan, Mercaptopurine,Mesna, Mesnex ™, Methotrexate, Methotrexate Sodium, Methylprednisolone,Meticorten ®, Mitomycin, Mitomycin-C, Mitoxantrone, M-Prednisol ®, MTC,MTX, Mustargen ®, Mustine, Mutamycin ®, Myleran ®, Mylocel ™,Mylotarg ®, Navelbine ®, Nelarabine, Neosar ®, Neulasta ™, Neumega ®,Neupogen ®, Nexavar ®, Nilandron ®, Nilutamide, Nipent ®, NitrogenMustard, Novaldex ®, Novantrone ®, Octreotide, Octreotide acetate,Oncospar ®, Oncovin ®, Ontak ®, Onxal ™, Oprevelkin, Orapred ®,Orasone ®, Oxaliplatin, Paclitaxel, Paclitaxel Protein-bound,Pamidronate, Panitumumab, Panretin ®, Paraplatin ®, Pediapred ®, PEGInterferon, Pegaspargase, Pegfilgrastim, PEG-INTRON ™,PEG-L-asparaginase, PEMETREXED, Pentostatin, Phenylalanine Mustard,Platinol ®, Platinol-AQ ®, Prednisolone, Prednisone, Prelone ®,Procarbazine, PROCRIT ®, Proleukin ®, Prolifeprospan 20 with CarmustineImplant, Purinethol ®, Raloxifene, Revlimid ®, Rheumatrex ®, Rituxan ®,Rituximab, Roferon-A ® (Interferon Alfa-2a), Rubex ®, Rubidomycinhydrochloride, Sandostatin ®, Sandostatin LAR ®, Sargramostim,Solu-Cortef ®, Solu-Medrol ®, Sorafenib, SPRYCEL ™, STI-571,Streptozocin, SU11248, Sunitinib, Sutent ®, Tamoxifen, Tarceva ®,Targretin ®, Taxol ®, Taxotere ®, Temodar ®, Temozolomide, Temsirolimus,Teniposide, TESPA, Thalidomide, Thalomid ®, TheraCys ®, Thioguanine,Thioguanine Tabloid ®, Thiophosphoamide, Thioplex ®, Thiotepa, TICE ®,Toposar ®, Topotecan, Toremifene, Torisel ®, Tositumomab, Trastuzumab,Treanda ®, Tretinoin, Trexall ™, Trisenox ®, TSPA, TYKERB ®, VCR,Vectibix ™, Velban ®, Velcade ®, VePesid ®, Vesanoid ®, Viadur ™,Vidaza ®, Vinblastine, Vinblastine Sulfate, Vincasar Pfs ®, Vincristine,Vinorelbine, Vinorelbine tartrate, VLB, VM-26, Vorinostat, VP-16,Vumon ®, Xeloda ®, Zanosar ®, Zevalin ™, Zinecard ®, Zoladex ®,Zoledronic acid, Zolinza, Zometa ® Combination CHOP (cyclophosphamide,doxorubicin, vincristine, and prednisone); CVP Therapies(cyclophosphamide, vincristine, and prednisone); RCVP (Rituximab + CVP);RCHOP (Rituximab + CHOP); RICE (Rituximab + ifosamide, carboplatin,etoposide); RDHAP, (Rituximab + dexamethasone, cytarabine, cisplatin);RESHAP (Rituximab + etoposide, methylprednisolone, cytarabine,cisplatin); combination treatment with vincristine, prednisone, andanthracycline, with or without asparaginase; combination treatment withdaunorubicin, vincristine, prednisone, and asparaginase; combinationtreatment with teniposide and Ara-C (cytarabine); combination treatmentwith methotrexate and leucovorin; combination treatment with bleomycin,doxorubicin, etoposide, mechlorethamine, prednisone, vinblastine, andvincristine; FOLFOX4 regimen (oxaliplatin, leucovorin, and fluorouracil[5-FU]); FOLFIRI regimen (Irinotecan Hydrochloride, Fluorouracil, andLeucovorin Calcium); Levamisole regimen (5-FU and levamisole); NCCTGregimen (5-FU and low-dose leucovorin); NSABP regimen (5-FU andhigh-dose leucovorin); XAD (Xelox (Capecitabine + Oxaliplatin) +Bevacizumab + Dasatinib); FOLFOX/Bevacizumab/Hydroxychloroquine; GermanAIO regimen (folic acid, 5-FU, and irinotecan); Douillard regimen (folicacid, 5-FU, and irinotecan); CAPOX regimen (Capecitabine, oxaliplatin);FOLFOX6 regimen (oxaliplatin, leucovorin, and 5-FU); FOLFIRI regimen(folic acid, 5-FU, and irinotecan); FUFOX regimen (oxaliplatin,leucovorin, and 5-FU); FUOX regimen (oxaliplatin and 5-FU); IFL regimen(irinotecan, 5-FU, and leucovorin); XELOX regimen (capecitabineoxaliplatin); KHAD-L (ketoconazole, hydrocortisone, dutasteride andlapatinib); Biologics anti-CD52 antibodies (e.g., Alemtuzumab),anti-CD20 antibodies (e.g., Rituximab), anti-CD40 antibodies (e.g.,SGN40) Classes of Anthracyclines and related substances, Anti-androgens,Anti-estrogens, Antigrowth Treatments hormones (e.g., Somatostatinanalogs), Combination therapy (e.g., vincristine, bcnu, melphalan,cyclophosphamide, prednisone (VBMCP)), DNA methyltransferase inhibitors,Endocrine therapy - Enzyme inhibitor, Endocrine therapy - other hormoneantagonists and related agents, Folic acid analogs (e.g., methotrexate),Folic acid analogs (e.g., pemetrexed), Gonadotropin releasing hormoneanalogs, Gonadotropin- releasing hormones, Monoclonal antibodies(EGFR-Targeted - e.g., panitumumab, cetuximab), Monoclonal antibodies(Her2-Targeted - e.g., trastuzumab), Monoclonal antibodies(Multi-Targeted - e.g., alemtuzumab), Other alkylating agents,Antineoplastic agents (e.g., asparaginase, ATRA, bexarotene, celecoxib,gemcitabine, hydroxyurea, irinotecan, topotecan, pentostatin), Cytotoxicantibiotics, Platinum compounds, Podophyllotoxin derivatives (e.g.,etoposide), Progestogens, Protein kinase inhibitors (EGFR-Targeted),Protein kinase inhibitors (Her2 targeted therapy - e.g., lapatinib),Pyrimidine analogs (e.g., cytarabine), Pyrimidine analogs (e.g.,fluoropyrimidines), Salicylic acid and derivatives (e.g., aspirin),Src-family protein tyrosine kinase inhibitors (e.g., dasatinib), Taxanes(e.g., nab-paclitaxel), Vinca Alkaloids and analogs, Vitamin D andanalogs, Monoclonal antibodies (Multi-Targeted - e.g., bevacizumab),Protein kinase inhibitors (e.g., imatinib, sorafenib, sunitinib)Prostate Cancer Watchful waiting (i.e., monitor without treatment);Surgery (e.g., Pelvic Treatments lymphadenectomy, Radical prostatectomy,Transurethral resection of the prostate (TURP); Orchiectomy); Radiationtherapy (e.g., external-beam radiation therapy (EBRT), Proton beamradiation; implantation of radioisotopes (i.e., iodine I 125, palladium,and iridium)); Hormone therapy (e.g., Luteinizing hormone-releasinghormone agonists such as leuprolide, goserelin, buserelin or ozarelix;Antiandrogens such as flutamide, 2-hydroxyflutamide, bicalutamide,megestrol acetate, nilutamide, ketoconazole, aminoglutethimide;calcitriol, gonadotropin-releasing hormone (GnRH), estrogens (DES,chlorotrianisene, ethinyl estradiol, conjugated estrogens USP, and DES-diphosphate), triptorelin, finasteride, cyproterone acetate, ASP3550);Cryosurgery/cryotherapy; Chemotherapy and Biologic therapy (dutasteride,zoledronate, azacitidine, docetaxel, prednisolone, celecoxib,atorvastatin, AMT2003, soy protein, LHRH agonist, PD-103, pomegranateextract, soy extract, taxotere, I-125, zoledronic acid, dasatinib,vitamin C, vitamin D, vitamin D3, vitamin E, gemcitabine, cisplatin,lenalidomide, prednisone, degarelix, OGX-011, OGX-427, MDV3100,tasquinimod, cabazitaxel, TOOKAD ®, lanreotide, PROSTVAC, GM-CSF,lenalidomide, samarium Sm-153 lexidronam, N-Methyl-D-Aspartate(NMDA)-Receptor Antagonist, sorafenib, sorafenib tosylate, mitoxantrone,ABI-008, hydrocortisone, panobinostat, soy-tomato extract, KHAD-L,TOK-001, cixutumumab, temsirolimus, ixabepilone, TAK-700, TAK-448,TRC105, cyclophosphamide, lenalidomide, MLN8237, GDC-0449, Alpharadin ®,ARN-509, PX-866, ISIS EIF4E Rx, AEZS-108, 131I-F16SIP MonoclonalAntibody, anti-OX40 antibody, Muscadine Plus, ODM-201, BBI608, ZD4054,erlotinib, rIL-2, epirubicin, estramustine phosphate, HuJ591-GSmonoclonal (177Lu-J591), abraxane, IVIG, fermented wheat germ nutriment(FWGE), 153Sm-EDTMP, estramustine, mitoxantrone, vinblastine,carboplatin, paclitaxel, pazopanib, cytarabine, testosteronereplacement, Zoledronic Acid, Strontium Chloride Sr 89, paricalcitol,satraplatin, RAD001 (everolimus), valproic acid, tea extract, Hamsa-1,hydroxychloroquine, sipuleucel-T, selenomethionine, selenium, lycopene,sunitinib, vandetanib, IMC-A12 antibody, monoclonal antibody IMC-3G3,ixabepilone, diindolylmethane, metformin, efavirenz, dasatinib,nilutamide, abiraterone, cabozantinib (XL184), isoflavines, cinacalcethydrochloride, SB939, LY2523355, KX2-391, olaparib, genestein, digoxin,RO4929097, ipilimumab, bafetinib, cediranib maleate, MK2206, phenelzinesulfate, triptorelin pamoate, saracatinib, STA-9090, tesetaxel,pasireotide, afatinib, GTx 758, lonafarnib, satraplatin, radiolabeledantibody 7E11, FP253/fludarabine, Coxsackie A21 (CVA21) virus, ARRY-380,ARRY-382, anti- PSMA designer T cells, pemetrexed disodium, bortezomib,MDX-1106, white button mushroom extract, SU011248, MLN9708, BMTP-11,ABT-888, CX-4945, 4SC-205, temozolomide, MGAH22, vinorelbine ditartrate,Sodium Selenite, vorinostat, Ad- REIC/Dkk-3, ASG-5ME, IMF-001,PROHIBITIN-TP01, DSTP3086S, ridaforolimus, MK-2206, MK-0752,polyunsaturated fatty acids, I-125, statins, cholecalciferol, omega- 3fatty acids, raloxifene, etoposide, POMELLA ™ extract, Lucrin depot);Cancer vaccines (e.g., DNA vaccines, peptide vaccines, dendritic cellvaccines, PEP223, PSA/TRICOM, PROSTVAC-V/TRICOM, PROSTVAC-F/TRICOM, PSAvaccine, TroVax ®, GI-6207, PSMA and TARP Peptide Vaccine); Ultrasound;Proton beam radiation Colorectal Cancer Primary Surgical Therapy (e.g.,local excision; resection and anastomosis of primary Treatments lesionand removal of surrounding lymph nodes); Adjuvant Therapy (e.g.,fluorouracil (5-FU), capecitabine, leucovorin, oxaliplatin, erlotinib,irinotecan, aspirin, mitomycin C, suntinib, cetuximab, bevacizumab,pegfilgrastim, panitumumab, ramucirumab, curcumin, celecoxib, FOLFOX4regimen, FOLFOX6 regimen, FOLFIRI regimen, FUFOX regimen, FUOX regimen,IFL regimen, XELOX regimen, 5-FU and levamisole regimens, German AIOregimen, CAPOX regimen, Douillard regimen, XAD, RAD001 (everolimus), ARQ197, BMS-908662, JI-101, hydroxychloroquine (HCQ), Yttrium Microspheres,EZN-2208, CS-7017, IMC-1121B, IMC-18F1, docetaxel, lonafarnib,Maytansinoid DM4-Conjugated Humanized Monoclonal Antibody huC242,paclitaxel, ARRY-380, ARRY-382, IMO-2055, MDX1105-01, CX-4945,Pazopanib, Ixabepilone, OSI-906, NPC-1C Chimeric Monoclonal Antibody,brivanib, Poly-ADP Ribose (PARP) Inhibitor, RO4929097, Anti-cancervaccine, CEA vaccine, cyclophosphamide, yttrium Y 90 DOTA anti-CEAmonoclonal antibody M5A, MEHD7945A, ABT-806, ABT-888, MEDI-565,LY2801653, AZD6244, PRI-724, BKM120, tivozanib, floxuridine,dexamethosone, NKTR-102, perifosine, regorafenib, EP0906, Celebrex,PHY906, KRN330, imatinib mesylate, azacitidine, entinostat, PX-866,ABX-EGF, BAY 43-9006, ESO-1 Lymphocytes and Aldesleukin, LBH589,olaparib, fostamatinib, PD 0332991, STA-9090, cholecalciferol, GI-4000,IL-12, AMG 706, temsirolimus, dulanermin, bortezomib, ursodiol,ridaforolimus, veliparib, NK012, Dalotuzumab, MK-2206, MK- 0752,lenalidomide, REOLYSIN ®, AUY922, PRI-724, BKM120, avastin, dasatinib);Adjuvant Radiation Therapy (particularly for rectal cancer)

As shown in Table 8, cancer treatments include various surgical andtherapeutic treatments. Anti-cancer agents include drugs such as smallmolecules and biologicals. The methods of the invention can be used toidentify a biosignature comprising circulating biomarkers that can thenbe used for theranostic purposes such as monitoring a treatmentefficacy, classifying a subject as a responder or non-responder to atreatment, or selecting a candidate therapeutic agent. The invention canbe used to provide a theranosis for any cancer treatments, includingwithout limitation thernosis involving the cancer treatments in Tables8-10. Cancer therapies that can be identified as candidate treatments bythe methods of the invention include without limitation thechemotherapeutic agents listed in Tables 8-10 and any appropriatecombinations thereof. In one embodiment, the treatments are specific fora specific type of cancer, such as the treatments listed for prostatecancer, colorectal cancer, breast cancer and lung cancer in Table 8. Inother embodiments, the treatments are specific for a tumor regardless ofits origin but that displays a certain biosignature, such as abiosignature comprising a marker listed in Tables 9-10, or a drugassociated marker listed in Table 11.

The invention provides methods of monitoring a cancer treatmentcomprising identifying a series of biosignatures in a subject over atime course, such as before and after a treatment, or over time afterthe treatment. The biosignatures are compared to a reference todetermine the efficacy of the treatment. In an embodiment, the treatmentis selected from Tables 8-10, such as radiation, surgery, chemotherapy,biologic therapy, neo-adjuvant therapy, adjuvant therapy, or watchfulwaiting. The reference can be from another individual or group ofindividuals or from the same subject. For example, a subject with abiosignature indicative of a cancer pre-treatment may have abiosignature indicative of a healthy state after a successful treatment.Conversely, the subject may have a biosignature indicative of cancerafter an unsuccessful treatment. The biosignatures can be compared overtime to determine whether the subject's biosignatures indicate animprovement, worsening of the condition, or no change. Additionaltreatments may be called for if the cancer is worsening or there is nochange over time. For example, hormone therapy may be used in additionto surgery or radiation therapy to treat more aggressive prostatecancers. One or more of the following miRs can be used in a biosignaturefor monitoring an efficacy of prostate cancer treatment: hsa-miR-1974,hsa-miR-27b, hsa-miR-103, hsa-miR-146a, hsa-miR-22, hsa-miR-382,hsa-miR-23a, hsa-miR-376c, hsa-miR-335, hsa-miR-142-5p, hsa-miR-221,hsa-miR-142-3p, hsa-miR-151-3p, hsa-miR-21, hsa-miR-16. One or more miRslisted in the following publication can be used in a biosignature formonitoring treatment of a cancer of the GI tract: Albulescu et al.,Tissular and soluble miRNAs for diagnostic and therapy improvement indigestive tract cancers, Exp Rev Mol Diag, 11:1, 101-120.

In some embodiments, the invention provides a method of identifying abiosignature in a sample from a subject in order to select a candidatetherapeutic. For example, the biosignature may indicate that adrug-associated target is mutated or differentially expressed, therebyindicating that the subject is likely to respond or not respond tocertain treatments. The candidate treatments can be chosen from theanti-cancer agents or classes of therapeutic agents identified in Tables8-10. In some embodiments, the candidate treatments identified accordingto the subject methods are chosen from at least the groups of treatmentsconsisting of 5-fluorouracil, abarelix, alemtuzumab, aminoglutethimide,anastrozole, asparaginase, aspirin, ATRA, azacitidine, bevacizumab,bexarotene, bicalutamide, calcitriol, capecitabine, carboplatin,celecoxib, cetuximab, chemotherapy, cholecalciferol, cisplatin,cytarabine, dasatinib, daunorubicin, decitabine, doxorubicin,epirubicin, erlotinib, etoposide, exemestane, flutamide, fulvestrant,gefitinib, gemcitabine, gonadorelin, goserelin, hydroxyurea, imatinib,irinotecan, lapatinib, letrozole, leuprolide, liposomal-doxorubicin,medroxyprogesterone, megestrol, megestrol acetate, methotrexate,mitomycin, nab-paclitaxel, octreotide, oxaliplatin, paclitaxel,panitumumab, pegaspargase, pemetrexed, pentostatin, sorafenib,sunitinib, tamoxifen, taxanes, temozolomide, toremifene, trastuzumab,VBMCP, and vincristine.

Similar to selecting a candidate treatment, the invention also providesa method of determining whether to treat a cancer at all. For example,prostate cancer can be a non-aggressive disease that is unlikely tosubstantially harm the subject. Radiation therapy with androgen ablation(hormone reduction) is the standard method of treating locally advancedprostate cancer. Morbidities of hormone therapy include impotence, hotflashes, and loss of libido. In addition, a treatment such asprostatectomy can have morbidities such as impotence or incontinence.Therefore, the invention provides biosignatures that indicateaggressiveness or a progression (e.g., stage or grade) of the cancer. Anon-aggressive cancer or localized cancer might not require immediatetreatment but rather be watched, e.g., “watchful waiting” of a prostatecancer. Whereas an aggressive or advanced stage lesion would require aconcomitantly more aggressive treatment regimen.

Examples of biomarkers that can be detected, and treatment agents thatcan be selected or possibly avoided are listed in Table 9. For example,a biosignature is identified for a subject with a prostate cancer,wherein the biosignature comprises levels of androgen receptor (AR).Overexpression or overproduction of AR, such as high levels of mRNAlevels or protein levels in a vesicle, provides an identification ofcandidate treatments for the subject. Such treatments include agents fortreating the subject such as Bicalutamide, Flutamide, Leuprolide, orGoserelin. The subject is accordingly identified as a responder toBicalutamide, Flutamide, Leuprolide, or Goserelin. In anotherillustrative example, BCRP mRNA, protein, or both is detected at highlevels in a vesicle from a subject suffering from NSCLC. The subject maythen be classified as a non-responder to the agents Cisplatin andCarboplatin, or the agents are considered to be less effective thanother agents for treating NSCLC in the subject and not selected for usein treating the subject. Any of the following biomarkers can be assessedin a vesicle obtained from a subject, and the biomarker can be in theform including but not limited to one or more of a nucleic acid,polypeptide, peptide or peptide mimetic. In yet another illustrativeexample, a mutation in one or more of KRAS, BRAF, PIK3CA, and/or c-kitcan be used to select a candidate treatment. For example, a mutation inKRAS or BRAF in a patient may indicate that cetuximab and/or panitumumabare likely to be less effective in treating the patient.

TABLE 9 Examples of Biomarkers, Lineage and Agents Possibly LessEffective Possible Agents to Biomarker Lineage Agents Consider AR (highexpression) Prostate Bicalutamide, Flutamide, Leuprolide, Goserelin AR(high expression) default Bicaluamide, Flutamide, Leuprolide, GoserelinBCRP (high Non-small cell lung cancer Cisplatin, Carboplatin expression)(NSCLC) BCRP (low Non-small cell lung cancer Cisplatin, Carboplatinexpression) (NSCLC) BCRP (high default Cisplatin, Carboplatinexpression) BCRP (low default Cisplatin, Carboplatin expression) BRAFV600E Colorectal Cetuximab, Panitumumab (mutation positive) BRAF V600EColorectal Cetuximab, Panitumumab (mutation negative) BRAF V600E Allother Cetuximab, Panitumumab (mutation positive) BRAF V600E All otherCetuximab, Panitumumab (mutation negative) BRAF V600E default Cetuximab,Panitumumab (mutation positive) BRAF V600E default Cetuximab,Panitumumab (mutation negative) CD52 (high Leukemia Alemtuzumabexpression) CD52 (low Leukemia Alemtuzumab expression) CD52 (highdefault (Hematologic Alemtuzumab expression) malignancies only) CD52(low default (Hematologic Alemtuzumab expression) malignancies only)c-kit Uveal Melanoma c-kit (high expression) Gastrointestinal StromalImatinib Tumors [GIST]; cKIT will not be performed on Uveal Melanoma asimatinib is not useful in the setting of WT cKIT positive uveal melanoma(see Hofmann et al. 2009) c-kit (high expression) Extrahepatic Bile DuctImatinib Tumors; cKIT will not be performed on Uveal Melanoma asimatinib is not useful in the setting of WT cKIT positive uveal melanoma(see Hofmann et al. 2009) c-kit (high expression) Acute myeloid leukemiaImatinib (AML) c-kit (high expression) default; cKIT will not beImatinib performed on Uveal Melanoma as imatinib is not useful in thesetting of WT cKIT positive uveal melanoma (see Hofmann et al. 2009)EGFR (high copy Head and neck squamous Erlotinib, Gefitinib number) cellcarcinoma (HNSCC) EGFR Head and neck squamous Erlotinib, Gefitinib cellcarcinoma (HNSCC) EGFR (high copy Non-small cell lung cancer Erlotinib,Gefitinib number) (NSCLC) EGFR (low copy Non-small cell lung cancerErlotinib, Gefitinib number) (NSCLC) EGFR (high copy default Cetuxumab,Panitumumab, number) Erlotinib, Gefitinib EGFR (low copy defaultCetuxumab, Panitumumab, number) Erlotinib, Gefitinib ER (highexpression) Breast Ixabepilone Tamoxifen-based treatment, aromataseinhibitors (anastrazole, letrozole) ER (low expression) BreastIxabepilone ER (high expression) Ovarian Tamoxifen-based treatment,aromatase inhibitors (anastrazole, letrozole) ER (high expression)default Tamoxifen-based treatment, aromatase inhibitors (anastrazole,letrozole) ERCC1 (high Non-small cell lung cancer Carboplatin, Cisplatinexpression) (NSCLC) ERCC1 (low Non-small cell lung cancer Carboplatin,Cisplatin expression) (NSCLC) ERCC1 (high Small Cell Lung CancerCarboplatin, Cisplatin expression) (SCLC) ERCC1 (low Small Cell LungCancer Carboplatin, Cisplatin expression) (SCLC) ERCC1 (high GastricOxaliplatin expression) ERCC1 (low Gastric Oxaliplatin expression) ERCC1(high default Carboplatin, Cisplatin, expression) Oxaliplatin ERCC1 (lowdefault Carboplatin, Cisplatin, expression) Oxaliplatin HER-2 (highBreast Lapatinib, Trastuzumab expression) HER-2 (high default Lapatinib,Trastuzumab expression) KRAS (mutation Colorectal cancer Cetuximab,Panitumumab positive) KRAS (mutation Colorectal cancer Cetuximab,Panitumumab negative) KRAS (mutation Non-small cell lung cancerErlotinib, Gefitinib positive) (NSCLC) KRAS (mutation Non-small celllung cancer Erlotinib, Gefitinib negative) (NSCLC) KRAS (mutationBronchioloalveolar Erlotinib positive) carcinoma (BAC) or adenocarcinoma(BAC subtype) KRAS (mutation Bronchioloalveolar Erlotinib negative)carcinoma (BAC) or adenocarcinoma (BAC subtype) KRAS (mutation Multiplemyeloma VBMCP/Cyclophosphamide positive) KRAS (mutation Multiple myelomaVBMCP/Cyclophosphamide negative) KRAS (mutation default Cetuximab,Panitumumab positive) KRAS (mutation default Cetuximab, panitumumabnegative) KRAS (mutation default Cetuximab, Erlotinib, positive)Panitumumab, Gefitinib KRAS (mutation default Cetuximab, Erlotinib,negative) Panitumumab, Gefitinib MGMT (high Pituitary tumors,Temozolomide expression) oligodendroglioma MGMT (low Pituitary tumors,Temozolomide expression) oligodendroglioma MGMT (high Neuroendocrinetumors Temozolomide expression) MGMT (low Neuroendocrine tumorsTemozolomide expression) MGMT (high default Temozolomide expression)MGMT (low default Temozolomide expression) MRP1 (high BreastCyclophosphamide expression) MRP1 (low Breast Cyclophosphamideexpression) MRP1 (high Small Cell Lung Cancer Etoposide expression)(SCLC) MRP1 (low Small Cell Lung Cancer Etoposide expression) (SCLC)MRP1 (high Nodal Diffuse Large B- Cyclophosphamide/Vincristineexpression) Cell Lymphoma MRP1 (low Nodal Diffuse Large B-Cyclophosphamide/Vincristine expression) Cell Lymphoma MRP1 (highdefault Cyclophosphamide, expression) Etoposide, Vincristine MRP1 (lowdefault Cyclophosphamide, expression) Etoposide, Vincristine PDGFRA(high Malignant Solitary Fibrous Imatinib expression) Tumor of thePleura (MSFT) PDGFRA (high Gastrointestinal stromal Imatinib expression)tumor (GIST) PDGFRA (high Default Imatinib expression) p-glycoprotein(high Acute myeloid leukemia Etoposide expression) (AML) p-glycoprotein(low Acute myeloid leukemia Etoposide expression) (AML) p-glycoprotein(high Diffuse Large B-cell Doxorubicin expression) Lymphoma (DLBCL)p-glycoprotein (low Diffuse Large B-cell Doxorubicin expression)Lymphoma (DLBCL) p-glycoprotein (high Lung Etoposide expression)p-glycoprotein (low Lung Etoposide expression) p-glycoprotein (highBreast Doxorubicin expression) p-glycoprotein (low Breast Doxorubicinexpression) p-glycoprotein (high Ovarian Paclitaxel expression)p-glycoprotein (low Ovarian Paclitaxel expression) p-glycoprotein (highHead and neck squamous Vincristine expression) cell carcinoma (HNSCC)p-glycoprotein (low Head and neck squamous Vincristine expression) cellcarcinoma (HNSCC) p-glycoprotein (high default Vincristine, Etoposide,expression) Doxorubicin, Paclitaxel p-glycoprotein (low defaultVincristine, Etoposide, expression) Doxorubicin, Paclitaxel PR (highexpression) Breast Chemoendocrine therapy Tamoxifen, Anastrazole,Letrozole PR (low expression) default Chemoendocrine therapy Tamoxifen,Anastrazole, Letrozole PTEN (high Breast Trastuzumab expression) PTEN(low Breast Trastuzumab expression) PTEN (high Non-small cell LungGefitinib expression) Cancer (NSCLC) PTEN (low Non-small cell LungGefitinib expression) Cancer (NSCLC) PTEN (high Colorectal Cetuximab,Panitumumab expression) PTEN (low Colorectal Cetuximab, Panitumumabexpression) PTEN (high Glioblastoma Erlotinib, Gefitinib expression)PTEN (low Glioblastoma Erlotinib, Gefitinib expression) PTEN (highdefault Cetuximab, Panitumumab, expression) Erlotinib, Gefitinib andTrastuzumab PTEN (low default Cetuximab, Panitumumab, expression)Erlotinib, Gefitinib and Trastuzumab RRM1 (high Non-small cell lungcancer Gemcitabine experssion) (NSCLC) RRM1 (low Non-small cell lungcancer Gemcitabine expression) (NSCLC) RRM1 (high Pancreas Gemcitabineexperssion) RRM1 (low Pancreas Gemcitabine expression) RRM1 (highdefault Gemcitabine experssion) RRM1 (low default Gemcitabineexpression) SPARC (high Breast nab-paclitaxel expression) SPARC (highdefault nab-paclitaxel expression) TS (high expression) Colorectalfluoropyrimidines TS (low expression) Colorectal fluoropyrimidines TS(high expression) Pancreas fluoropyrimidines TS (low expression)Pancreas fluoropyrimidines TS (high expression) Head and Neck Cancerfluoropyrimidines TS (low expression) Head and Neck Cancerfluoropyrimidines TS (high expression) Gastric fluoropyrimidines TS (lowexpression) Gastric fluoropyrimidines TS (high expression) Non-smallcell lung cancer fluoropyrimidines (NSCLC) TS (low expression) Non-smallcell lung cancer fluoropyrimidines (NSCLC) TS (high expression) Liverfluoropyrimidines TS (low expression) Liver fluoropyrimidines TS (highexpression) default fluoropyrimidines TS (low expression) defaultfluoropyrimidines TOPO1 (high Colorectal Irinotecan expression) TOPO1(low Colorectal Irinotecan expression) TOPO1 (high Ovarian Irinotecanexpression) TOPO1 (low Ovarian Irinotecan expression) TOPO1 (highdefault Irinotecan expression) TOPO1 (low default Irinotecan expression)TopoIIa (high Breast Doxorubicin, liposomal- epxression) Doxorubicin,Epirubicin TopoIIa (low Breast Doxorubicin, liposomal- expression)Doxorubicin, Epirubicin TopoIIa (high default Doxorubicin, liposomal-epxression) Doxorubicin, Epirubicin TopoIIa (low default Doxorubicin,liposomal- expression) Doxorubicin, Epirubicin

Other examples of biomarkers that can be detected and the treatmentagents that can be selected or possibly avoided based on the biomarkersignatures are listed in Table 10. For example, for a subject sufferingfrom cancer, detecting overexpression of ADA in vesicles from a subjectis used to classify the subject as a responder to pentostatin, orpentostatin identified as an agent to use for treating the subject. Inanother example, for a subject suffering from cancer, detectingoverexpression of BCRP in vesicles from the subject is used to classifythe subject as a non-responder to cisplatin, carboplatin, irinotecan,and topotecan, meaning that cisplatin, carboplatin, irinotecan, andtopotecan are identified as agents that are suboptimal for treating thesubject.

TABLE 10 Examples of Biomarkers, Agents and Resistance Gene NameExpression Status Candidate Agent(s) Possible Resistance ADAOverexpressed pentostatin ADA Underexpressed cytarabine AR Overexpressedabarelix, bicalutamide, flutamide, gonadorelin, goserelin, leuprolideASNS Underexpressed asparaginase, pegaspargase BCRP (ABCG2)Overexpressed cisplatin, carboplatin, irinotecan, topotecan BRCA1Underexpressed mitomycin BRCA2 Underexpressed mitomycin CD52Overexpressed alemtuzumab CDA Overexpressed cytarabine c-erbB2 Highlevels of Trastuzumab, c-erbB2 phosphorylation in kinase inhibitor,lapatinib epithelial cells CES2 Overexpressed irinotecan c-kitOverexpressed sorafenib, sunitinib, imatinib COX-2 Overexpressedcelecoxib DCK Overexpressed gemcitabine cytarabine DHFR Underexpressedmethotrexate, pemetrexed DHFR Overexpressed methotrexate DNMT1Overexpressed azacitidine, decitabine DNMT3A Overexpressed azacitidine,decitabine DNMT3B Overexpressed azacitidine, decitabine EGFROverexpressed erlotinib, gefitinib, cetuximab, panitumumab EML4-ALKOverexpressed (present) crizotinib EPHA2 Overexpressed dasatinib EROverexpressed anastrazole, exemestane, fulvestrant, letrozole,megestrol, tamoxifen, medroxyprogesterone, toremifene, aminoglutethimideERCC1 Overexpressed carboplatin, cisplatin GART Underexpressedpemetrexed GRN (PCDGF, PGRN) Overexpressed anti-oestrogen therapy,tamoxifen, faslodex, letrozole, herceptin in Her-2 overexpressing cells,doxorubicin HER-2 (ERBB2) Overexpressed trastuzumab, lapatinib HIF-1αOverexpressed sorafenib, sunitinib, bevacizumab IκB-α Overexpressedbortezomib MGMT Underexpressed temozolomide MGMT Overexpressedtemozolomide MRP1 (ABCC1) Overexpressed etoposide, paclitaxel,docetaxel, vinblastine, vinorelbine, topotecan, teniposide P-gp (ABCB1)Overexpressed doxorubicin, etoposide, epirubicin, paclitaxel, docetaxel,vinblastine, vinorelbine, topotecan, teniposide, liposomal doxorubicinPDGFR-α Overexpressed sorafenib, sunitinib, imatinib PDGFR-βOverexpressed sorafenib, sunitinib, imatinib PR Overexpressedexemestane, fulvestrant, gonadorelin, goserelin, medroxyprogesterone,megestrol, tamoxifen, toremifene RARA Overexpressed ATRA RRM1Underexpressed gemcitabine, hydroxyurea RRM2 Underexpressed gemcitabine,hydroxyurea RRM2B Underexpressed gemcitabine, hydroxyurea RXR-αOverexpressed bexarotene RXR-β Overexpressed bexarotene SPARCOverexpressed nab-paclitaxel SRC Overexpressed dasatinib SSTR2Overexpressed octreotide SSTR5 Overexpressed octreotide TOPO IOverexpressed irinotecan, topotecan TOPO IIα Overexpressed doxorubicin,epirubicin, liposomal-doxorubicin TOPO IIβ Overexpressed doxorubicin,epirubicin, liposomal-doxorubicin TS Underexpressed capecitabine, 5-fluorouracil, pemetrexed TS Overexpressed capecitabine, 5- fluorouracilVDR Overexpressed calcitriol, cholecalciferol VEGFR1 (Flt1)Overexpressed sorafenib, sunitinib, bevacizumab VEGFR2 Overexpressedsorafenib, sunitinib, bevacizumab VHL Underexpressed sorafenib,sunitinib

Further drug associations and rules that are used in embodiments of theinvention are found in U.S. patent application Ser. No. 12/658,770,filed Feb. 12, 2010; International PCT Patent ApplicationPCT/US2010/000407, filed Feb. 11, 2010; International PCT PatentApplication PCT/US2010/54366, filed Oct. 27, 2010; and U.S. ProvisionalPatent Application 61/427,788, filed Dec. 28, 2010; all of whichapplications are incorporated by reference herein in their entirety.See, e.g., “Table 4: Rules Summary for Treatment Selection” ofPCT/US2010/54366.

Any drug-associated target can be part of a biosignature for providing atheranosis. A “druggable target” comprising a target that can bemodulated with a therapeutic agent such as a small molecule or biologic,is a candidate for inclusion in the biosignature of the invention.Drug-associated targets also include biomarkers that can conferresistance to a treatment, such as shown in Tables 9 and 10. Thebiosignature can be based on either the gene, e.g., DNA sequence, and/orgene product, e.g., mRNA or protein, or the drug-associated target. Suchnucleic acid and/or polypeptide can be profiled as applicable as topresence or absence, level or amount, activity, mutation, sequence,haplotype, rearrangement, copy number, or other measurablecharacteristic. The gene or gene product can be associated with avesicle population, e.g., as a vesicle surface marker or as vesiclepayload. In an embodiment, the invention provides a method oftheranosing a cancer, comprising identifying a biosignature thatcomprises a presence or level of one or more drug-associated target, andselecting a candidate therapeutic based on the biosignature. Thedrug-associated target can be a circulating biomarker, a vesicle, or avesicle associated biomarker. Because drug-associated targets can beindependent of the tissue or cell-of-origin, biosignatures comprisingdrug-associated targets can be used to provide a theranosis for anyproliferative disease, such as cancers from various anatomical origins,including cancers of unknown origin such as CUPS.

The drug-associated targets assessed using the methods of the inventioncomprise without limitation ABCC1, ABCG2, ACE2, ADA, ADH1C, ADH4, AGT,AR, AREG, ASNS, BCL2, BCRP, BDCA1, beta III tubulin, BIRC5, B-RAF,BRCA1, BRCA2, CA2, caveolin, CD20, CD25, CD33, CD52, CDA, CDKN2A,CDKN1A, CDKN1B, CDK2, CDW52, CES2, CK 14, CK 17, CK 5/6, c-KIT, c-Met,c-Myc, COX-2, Cyclin D1, DCK, DHFR, DNMT1, DNMT3A, DNMT3B, E-Cadherin,ECGF1, EGFR, EML4-ALK fusion, EPHA2, Epiregulin, ER, ERBR2, ERCC1,ERCC3, EREG, ESR1, FLT1, folate receptor, FOLR1, FOLR2, FSHB, FSHPRH1,FSHR, FYN, GART, GNRH1, GNRHR1, GSTP1, HCK, HDAC1, hENT-1, Her2/Neu,HGF, HIF1A, HIG1, HSP90, HSP90AA1, HSPCA, IGF-1R, IGFRBP, IGFRBP3,IGFRBP4, IGFRBP5, IL13RA1, IL2RA, KDR, Ki67, KIT, K-RAS, LCK, LTB,Lymphotoxin Beta Receptor, LYN, MET, MGMT, MLH1, MMR, MRP1, MS4A1, MSH2,MSH5, Myc, NFKB1, NFKB2, NFKBIA, ODC1, OGFR, p16, p21, p27, p53, p95,PARP-1, PDGFC, PDGFR, PDGFRA, PDGFRB, PGP, PGR, PI3K, POLA, POLA1,PPARG, PPARGC1, PR, PTEN, PTGS2, RAF1, RARA, RRM1, RRM2, RRM2B, RXRB,RXRG, SPARC, SRC, SSTR1, SSTR2, SSTR3, SSTR4, SSTR5, Survivin, TK1,TLE3, TNF, TOP1, TOP2A, TOP2B, TS, TXN, TXNRD1, TYMS, VDR, VEGF, VEGFA,VEGFC, VHL, YES 1, ZAP70, or any combination thereof. A biosignatureincluding one or combination of these markers can be used tocharacterize a phenotype according to the invention, such as providing atheranosis. These markers are known to play a role in the efficacy ofvarious chemotherapeutic agents against proliferative diseases.Accordingly, the markers can be assessed to select a candidate treatmentfor the cancer independent of the origin or type of cancer. In anembodiment, the invention provides a method of selecting a candidatetherapeutic for a cancer, comprising identifying a biosignaturecomprising a level or presence of one or more drug associated target,and selecting the candidate therapeutic based on its predicted efficacyfor a patient with the biosignature. The one or more drug-associatedtarget can be one of the targets listed above, or in Tables 9-11. Insome embodiments, at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, 25,30, 35, 40, 45, or at least 50 of the one or more drug-associatedtargets are assessed. The one or more drug-associated target can beassociated with a vesicle, e.g., as a vesicle surface marker or asvesicle payload as either nucleic acid (e.g., DNA, mRNA) or protein. Insome embodiments, the presence or level of a microRNA known to interactwith the one or more drug-associated target is assessed, wherein a highlevel of microRNA known to suppress the one or more drug-associatedtarget can indicate a lower expression of the one or moredrug-associated target and thus a lower likelihood of response to atreatment against the drug-associated target. The one or moredrug-associated target can be circulating biomarkers. The one or moredrug-associated target can be assessed in a tissue sample. The predictedefficacy can be determined by comparing the presence or level of the oneor more drug-associated target to a reference value, wherein a higherlevel that the reference indicates that the subject is a likelyresponder. The predicted efficacy can be determined using a classifieralgorithm, wherein the classifier was trained by comparing thebiosignature of the one or more drug-associated target in subjects thatare known to be responders or non-responders to the candidate treatment.Molecular associations of the one or more drug-associated target withappropriate candidate targets are displayed in Tables 9-10 herein andU.S. patent application Ser. No. 12/658,770, filed Feb. 12, 2010;International PCT Patent Application PCT/US2010/000407, filed Feb. 11,2010; International PCT Patent Application PCT/US2010/54366, filed Oct.27, 2010; and U.S. Provisional Patent Application 61/427,788, filed Dec.28, 2010; all of which applications are incorporated by reference hereinin their entirety.

Table 11 provides a listing of gene and corresponding protein symbolsand names of many of the theranostic targets that are analyzed accordingto the methods of the invention. As understood by those of skill in theart, genes and proteins have developed a number of alternative names inthe scientific literature. Thus, the listing in Table 11 comprises anillustrative but not exhaustive compilation. A further listing of genealiases and descriptions can be found using a variety of onlinedatabases, including GeneCards® (www.genecards.org), HUGO GeneNomenclature (www.genenames.org), Entrez Gene(www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene), UniProtKB/Swiss-Prot(www.uniprot.org), UniProtKB/TrEMBL (www.uniprot.org), OMIM(www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIM), GeneLoc(genecards.weizmann.ac.il/geneloc/), and Ensembl (www.ensembl.org).Generally, gene symbols and names below correspond to those approved byHUGO, and protein names are those recommended by UniProtKB/Swiss-Prot.Common alternatives are provided as well. Where a protein name indicatesa precursor, the mature protein is also implied. Throughout theapplication, gene and protein symbols may be used interchangeably andthe meaning can be derived from context as necessary.

TABLE 11 Genes and Related Proteins for Cancer Theranostics Gene ProteinSymbol Gene Name Symbol Protein Name ABCB1, ATP-binding cassette,sub-family B ABCB1, Multidrug resistance protein 1; P- PGP (MDR/TAP),member 1 MDR1, PGP glycoprotein ABCC1, ATP-binding cassette, sub-familyC MRP1, Multidrug resistance-associated protein 1 MRP1 (CFTR/MRP),member 1 ABCC1 ABCG2, ATP-binding cassette, sub-family G ABCG2ATP-binding cassette sub-family G BCRP (WHITE), member 2 member 2 ACE2angiotensin I converting enzyme ACE2 Angiotensin-converting enzyme 2(peptidyl-dipeptidase A) 2 precursor ADA adenosine deaminase ADAAdenosine deaminase ADH1C alcohol dehydrogenase 1C (class I), ADH1GAlcohol dehydrogenase 1C gamma polypeptide ADH4 alcohol dehydrogenase 4(class II), pi ADH4 Alcohol dehydrogenase 4 polypeptide AGTangiotensinogen (serpin peptidase ANGT, Angiotensinogen precursorinhibitor, clade A, member 8) AGT ALK anaplastic lymphoma receptortyrosine ALK ALK tyrosine kinase receptor precursor kinase AR androgenreceptor AR Androgen receptor AREG amphiregulin AREG Amphiregulinprecursor ASNS asparagine synthetase ASNS Asparagine synthetase[glutamine- hydrolyzing] BCL2 B-cell CLL/lymphoma 2 BCL2 Apoptosisregulator Bcl-2 BDCA1, CD1c molecule CD1C T-cell surface glycoproteinCD1c precursor CD1C BIRC5 baculoviral IAP repeat-containing 5 BIRC5,Baculoviral IAP repeat-containing protein Survivin 5; Survivin BRAFv-raf murine sarcoma viral oncogene B-RAF, Serine/threonine-proteinkinase B-raf homolog B1 BRAF BRCA1 breast cancer 1, early onset BRCA1Breast cancer type 1 susceptibility protein BRCA2 breast cancer 2, earlyonset BRCA2 Breast cancer type 2 susceptibility protein CA2 carbonicanhydrase II CA2 Carbonic anhydrase 2 CAV1 caveolin 1, caveolae protein,22 kDa CAV1 Caveolin-1 CCND1 cyclin D1 CCND1, G1/S-specific cyclin-D1Cyclin D1, BCL-1 CD20, membrane-spanning 4-domains, CD20 B-lymphocyteantigen CD20 MS4A1 subfamily A, member 1 CD25, interleukin 2 receptor,alpha CD25 Interleukin-2 receptor subunit alpha IL2RA precursor CD33CD33 molecule CD33 Myeloid cell surface antigen CD33 precursor CD52,CD52 molecule CD52 CAMPATH-1 antigen precursor CDW52 CDA cytidinedeaminase CDA Cytidine deaminase CDH1, cadherin 1, type 1, E-cadherinE-Cad Cadherin-1 precursor (E-cadherin) ECAD (epithelial) CDK2cyclin-dependent kinase 2 CDK2 Cell division protein kinase 2 CDKN1A,cyclin-dependent kinase inhibitor 1A CDKN1A, Cyclin-dependent kinaseinhibitor 1 P21 (p21, Cip1) p21 CDKN1B cyclin-dependent kinase inhibitor1B CDKN1B, Cyclin-dependent kinase inhibitor 1B (p27, Kip1) p27 CDKN2A,cyclin-dependent kinase inhibitor 2A CD21A, p16 Cyclin-dependent kinaseinhibitor 2A, P16 (melanoma, p16, inhibits CDK4) isoforms 1/2/3 CES2carboxylesterase 2 (intestine, liver) CES2, EST2 Carboxylesterase 2precursor CK 5/6 cytokeratin 5/cytokeratin 6 CK 5/6 Keratin, type IIcytoskeletal 5; Keratin, type II cytoskeletal 6 CK14, keratin 14 CK14Keratin, type I cytoskeletal 14 KRT14 CK17, keratin 17 CK17 Keratin,type I cytoskeletal 17 KRT17 COX2, prostaglandin-endoperoxide synthase 2COX-2, Prostaglandin G/H synthase 2 precursor PTGS2 (prostaglandin G/Hsynthase and PTGS2 cyclooxygenase) DCK deoxycytidine kinase DCKDeoxycytidine kinase DHFR dihydrofolate reductase DHFR Dihydrofolatereductase DNMT1 DNA (cytosine-5-)-methyltransferase 1 DNMT1 DNA(cytosine-5)-methyltransferase 1 DNMT3A DNA(cytosine-5-)-methyltransferase 3 DNMT3A DNA(cytosine-5)-methyltransferase 3A alpha DNMT3B DNA(cytosine-5-)-methyltransferase 3 DNMT3B DNA(cytosine-5)-methyltransferase 3B beta ECGF1, thymidine phosphorylaseTYMP, PD- Thymidine phosphorylase precursor TYMP ECGF, ECDF1 EGFR,epidermal growth factor receptor EGFR, Epidermal growth factor receptorERBB1, (erythroblastic leukemia viral (v-erb-b) ERBB1, precursor HER1oncogene homolog, avian) HER1 EML4 echinoderm microtubule associatedEML4 Echinoderm microtubule-associated protein like 4 protein-like 4EPHA2 EPH receptor A2 EPHA2 Ephrin type-A receptor 2 precursor ER, ESR1estrogen receptor 1 ER, ESR1 Estrogen receptor ERBB2, v-erb-b2erythroblastic leukemia viral ERBB2, Receptor tyrosine-protein kinaseerbB-2 HER2/NEU oncogene homolog 2, HER2, precursor neuro/glioblastomaderived oncogene HER-2/neu homolog (avian) ERCC1 excision repaircross-complementing ERCC1 DNA excision repair protein ERCC-1 rodentrepair deficiency, complementation group 1 (includes overlappingantisense sequence) ERCC3 excision repair cross-complementing ERCC3TFIIH basal transcription factor complex rodent repair deficiency,helicase XPB subunit complementation group 3 (xeroderma pigmentosumgroup B complementing) EREG Epiregulin EREG Proepiregulin precursor FLT1fms-related tyrosine kinase 1 (vascular FLT-1, Vascular endothelialgrowth factor receptor endothelial growth factor/vascular VEGFR1 1precursor permeability factor receptor) FOLR1 folate receptor 1 (adult)FOLR1 Folate receptor alpha precursor FOLR2 folate receptor 2 (fetal)FOLR2 Folate receptor beta precursor FSHB follicle stimulating hormone,beta FSHB Follitropin subunit beta precursor polypeptide FSHPRH1,centromere protein I FSHPRH1, Centromere protein I CENP1 CENP1 FSHRfollicle stimulating hormone receptor FSHR Follicle-stimulating hormonereceptor precursor FYN FYN oncogene related to SRC, FGR, FYNTyrosine-protein kinase Fyn YES GART phosphoribosylglycinamide GART,Trifunctional purine biosynthetic protein formyltransferase, PUR2adenosine-3 phosphoribosylglycinamide synthetase,phosphoribosylaminoimidazole synthetase GNRH1 gonadotropin-releasinghormone 1 GNRH1, Progonadoliberin-1 precursor (luteinizing-releasinghormone) GON1 GNRHR1, gonadotropin-releasing hormone GNRHR1Gonadotropin-releasing hormone receptor GNRHR receptor GSTP1 glutathioneS-transferase pi 1 GSTP1 Glutathione S-transferase P HCK hemopoieticcell kinase HCK Tyrosine-protein kinase HCK HDAC1 histone deacetylase 1HDAC1 Histone deacetylase 1 HGF hepatocyte growth factor (hepapoietinHGF Hepatocyte growth factor precursor A; scatter factor) HIF1A hypoxiainducible factor 1, alpha HIF1A Hypoxia-inducible factor 1-alpha subunit(basic helix-loop-helix transcription factor) HIG1, HIG1 hypoxiainducible domain HIG1, HIG1 domain family member 1A HIGD1A, family,member 1A HIGD1A, HIG1A HIG1A HSP90AA1, heat shock protein 90 kDa alphaHSP90, Heat shock protein HSP 90-alpha HSP90, (cytosolic), class Amember 1 HSP90A HSPCA IGF1R insulin-like growth factor 1 receptor IGF-1RInsulin-like growth factor 1 receptor precursor IGFBP3, insulin-likegrowth factor binding IGFBP-3, Insulin-like growth factor-bindingprotein IGFRBP3 protein 3 IBP-3 3 precursor IGFBP4, insulin-like growthfactor binding IGFBP-4, Insulin-like growth factor-binding proteinIGFRBP4 protein 4 IBP-4 4 precursor IGFBP5, insulin-like growth factorbinding IGFBP-5, Insulin-like growth factor-binding protein IGFRBP5protein 5 IBP-5 5 precursor IL13RA1 interleukin 13 receptor, alpha 1IL-13RA1 Interleukin-13 receptor subunit alpha-1 precursor KDR kinaseinsert domain receptor (a type KDR, Vascular endothelial growth factorreceptor III receptor tyrosine kinase) VEGFR2 2 precursor KIT, c-KITv-kit Hardy-Zuckerman 4 feline KIT, c-KIT Mast/stem cell growth factorreceptor sarcoma viral oncogene homolog precursor KRAS v-Ki-ras2 Kirstenrat sarcoma viral K-RAS GTPase KRas precursor oncogene homolog LCKlymphocyte-specific protein tyrosine LCK Tyrosine-protein kinase Lckkinase LTB lymphotoxin beta (TNF superfamily, LTB, TNF3 Lymphotoxin-betamember 3) LTBR lymphotoxin beta receptor (TNFR LTBR, Tumor necrosisfactor receptor superfamily superfamily, member 3) LTBR3, member 3precursor TNFR LYN v-yes-1 Yamaguchi sarcoma viral LYN Tyrosine-proteinkinase Lyn related oncogene homolog MET, c- met proto-oncogene(hepatocyte MET, c- Hepatocyte growth factor receptor MET growth factorreceptor) MET precursor MGMT O-6-methylguanine-DNA MGMTMethylated-DNA--protein-cysteine methyltransferase methyltransferaseMKI67, antigen identified by monoclonal Ki67, Ki-67 Antigen KI-67 KI67antibody Ki-67 MLH1 mutL homolog 1, colon cancer, MLH1 DNA mismatchrepair protein Mlh1 nonpolyposis type 2 (E. coli) MMR mismatch repair(refers to MLH1, MSH2, MSH5) MSH2 mutS homolog 2, colon cancer, MSH2 DNAmismatch repair protein Msh2 nonpolyposis type 1 (E. coli) MSH5 mutShomolog 5 (E. coli) MSH5, MutS protein homolog 5 hMSH5 MYC, c- v-mycmyelocytomatosis viral MYC, c- Myc proto-oncogene protein MYC oncogenehomolog (avian) MYC NBN, P95 nibrin NBN, p95 Nibrin NDGR1 N-mycdownstream regulated 1 NDGR1 Protein NDGR1 NFKB1 nuclear factor of kappalight NFKB1 Nuclear factor NF-kappa-B p105 subunit polypeptide geneenhancer in B-cells 1 NFKB2 nuclear factor of kappa light NFKB2 Nuclearfactor NF-kappa-B p100 subunit polypeptide gene enhancer in B-cells 2(p49/p100) NFKBIA nuclear factor of kappa light NFKBIA NF-kappa-Binhibitor alpha polypeptide gene enhancer in B-cells inhibitor, alphaODC1 ornithine decarboxylase 1 ODC Ornithine decarboxylase OGFR opioidgrowth factor receptor OGFR Opioid growth factor receptor PARP1 poly(ADP-ribose) polymerase 1 PARP-1 Poly [ADP-ribose] polymerase 1 PDGFCplatelet derived growth factor C PDGF-C, Platelet-derived growth factorC precursor VEGF-E PDGFR platelet-derived growth factor receptor PDGFRPlatelet-derived growth factor receptor PDGFRA platelet-derived growthfactor receptor, PDGFRA, Alpha-type platelet-derived growth factor alphapolypeptide PDGFR2, receptor precursor CD140 A PDGFRB platelet-derivedgrowth factor receptor, PDGFRB, Beta-type platelet-derived growth factorbeta polypeptide PDGFR, receptor precursor PDGFR1, CD140 B PIK3CAphosphoinositide-3-kinase, catalytic, PI3K phosphoinositide-3-kinase,catalytic, alpha alpha polypeptide subunit polypeptide p110α PSMD9,proteasome (prosome, macropain) 26S p27 26S proteasome non-ATPaseregulatory P27 subunit, non-ATPase, 9 subunit 9 PTEN phosphatase andtensin homolog RRM1 ribonucleotide reductase M1 RRM1, RR1Ribonucleoside-diphosphate reductase large subunit RRM2 ribonucleotidereductase M2 RRM2, Ribonucleoside-diphosphate reductase RR2M, RR2subunit M2 RRM2B ribonucleotide reductase M2 B (TP53 RRM2B,Ribonucleoside-diphosphate reductase inducible) P53R2 subunit M2 B RXRBretinoid X receptor, beta RXRB Retinoic acid receptor RXR-beta RXRGretinoid X receptor, gamma RXRG, Retinoic acid receptor RXR-gamma RXRCSIK2 salt-inducible kinase 2 SIK2, Salt-inducible protein kinase 2;Q9H0K1 Serine/threonine-protein kinase SIK2 SLC29A1 solute carrierfamily 29 (nucleoside ENT-1 Equilibrative nucleoside transporter 1transporters), member 1 SPARC secreted protein, acidic, cysteine-richSPARC SPARC precursor; Osteonectin (osteonectin) SRC v-src sarcoma(Schmidt-Ruppin A-2) SRC Proto-oncogene tyrosine-protein kinase viraloncogene homolog (avian) Src SSTR1 somatostatin receptor 1 SSTR1,Somatostatin receptor type 1 SSR1, SS1R SSTR2 somatostatin receptor 2SSTR2, Somatostatin receptor type 2 SSR2, SS2R SSTR3 somatostatinreceptor 3 SSTR3, Somatostatin receptor type 3 SSR3, SS3R SSTR4somatostatin receptor 4 SSTR4, Somatostatin receptor type 4 SSR4, SS4RSSTR5 somatostatin receptor 5 SSTR5, Somatostatin receptor type 5 SSR5,SS5R TK1 thymidine kinase 1, soluble TK1, KITH Thymidine kinase,cytosolic TLE3 transducin-like enhancer of split 3 TLE3 Transducin-likeenhancer protein 3 (E(sp1) homolog, Drosophila) TNF tumor necrosisfactor (TNF TNF, TNF- Tumor necrosis factor precursor superfamily,member 2) alpha, TNF-a TOP1, topoisomerase (DNA) I TOP1, DNAtopoisomerase 1 TOPO1 TOPO1 TOP2A, topoisomerase (DNA) II alpha 170 kDaTOP2A, DNA topoisomerase 2-alpha; TOPO2A TOP2, Topoisomerase II alphaTOPO2A TOP2B, topoisomerase (DNA) II beta 180 kDa TOP2B, DNAtopoisomerase 2-beta; TOPO2B TOPO2B Topoisomerase II beta TP53 tumorprotein p53 p53 Cellular tumor antigen p53 TUBB3 tubulin, beta 3 BetaIII Tubulin beta-3 chain tubulin, TUBB3, TUBB4 TXN thioredoxin TXN, TRX,Thioredoxin TRX-1 TXNRD1 thioredoxin reductase 1 TXNRD1, Thioredoxinreductase 1, cytoplasmic; TXNR Oxidoreductase TYMS, TS thymidylatesynthetase TYMS, TS Thymidylate synthase VDR vitamin D(1,25-dihydroxyvitamin D3) VDR Vitamin D3 receptor receptor VEGFA,vascular endothelial growth factor A VEGF-A, Vascular endothelial growthfactor A VEGF VEGF precursor VEGFC vascular endothelial growth factor CVEGF-C Vascular endothelial growth factor C precursor VHL vonHippel-Lindau tumor suppressor VHL Von Hippel-Lindau disease tumorsuppressor YES1 v-yes-1 Yamaguchi sarcoma viral YES1, Yes,Proto-oncogene tyrosine-protein kinase oncogene homolog 1 p61-Yes YesZAP70 zeta-chain (TCR) associated protein ZAP-70 Tyrosine-protein kinaseZAP-70 kinase 70 kDa

Genes and gene products that are known to play a role in cancer and canbe included in a biosignature of the invention include withoutlimitation 2AR, A DISINTEGRIN, ACTIVATOR OF THYROID AND RETINOIC ACIDRECEPTOR (ACTR), ADAM 11, ADIPOGENESIS INHIBITORY FACTOR (ADIF), ALPHA 6INTEGRIN SUBUNIT, ALPHA V INTEGRIN SUBUNIT, ALPHA-CATENIN, AMPLIFIED INBREAST CANCER 1 (AIB1), AMPLIFIED IN BREAST CANCER 3 (AIB3), AMPLIFIEDIN BREAST CANCER 4 (AIB4), AMYLOID PRECURSOR PROTEIN SECRETASE (APPS),AP-2 GAMMA, APPS, ATP-BINDING CASSETTE TRANSPORTER (ABCT),PLACENTA-SPECIFIC (ABCP), ATP-BINDING CASSETTE SUBFAMILY C MEMBER(ABCC1), BAG-1, BASIGIN (BSG), BCEI, B-CELL DIFFERENTIATION FACTOR(BCDF), B-CELL LEUKEMIA 2 (BCL-2), B-CELL STIMULATORY FACTOR-2 (BSF-2),BCL-1, BCL-2-ASSOCIATED X PROTEIN (BAX), BCRP, BETA 1 INTEGRIN SUBUNIT,BETA 3 INTEGRIN SUBUNIT, BETA 5 INTEGRIN SUBUNIT, BETA-2 INTERFERON,BETA-CATENIN, BETA-CATENIN, BONE SIALOPROTEIN (BSP), BREAST CANCERESTROGEN-INDUCIBLE SEQUENCE (BCEI), BREAST CANCER RESISTANCE PROTEIN(BCRP), BREAST CANCER TYPE 1 (BRCA1), BREAST CANCER TYPE 2 (BRCA2),BREAST CARCINOMA AMPLIFIED SEQUENCE 2 (BCAS2), CADHERIN, EPITHELIALCADHERIN-11, CADHERIN-ASSOCIATED PROTEIN, CALCITONIN RECEPTOR (CTR),CALCIUM PLACENTAL PROTEIN (CAPL), CALCYCLIN, CALLA, CAM5, CAPL,CARCINOEMBRYONIC ANTIGEN (CEA), CATENIN, ALPHA 1, CATHEPSIN B, CATHEPSIND, CATHEPSIN K, CATHEPSIN L2, CATHEPSIN O, CATHEPSIN O1, CATHEPSIN V,CD10, CD146, CD147, CD24, CD29, CD44, CD51, CD54, CD61, CD66e, CD82,CD87, CD9, CEA, CELLULAR RETINOL-BINDING PROTEIN 1 (CRBP1), c-ERBB-2,CK7, CK8, CK18, CK19, CK20, CLAUDIN-7, c-MET, COLLAGENASE, FIBROBLAST,COLLAGENASE, INTERSTITIAL, COLLAGENASE-3, COMMON ACUTE LYMPHOCYTICLEUKEMIA ANTIGEN (CALLA), CONNEXIN 26 (Cx26), CONNEXIN 43 (Cx43),CORTACTIN, COX-2, CTLA-8, CTR, CTSD, CYCLIN D1, CYCLOOXYGENASE-2,CYTOKERATIN 18, CYTOKERATIN 19, CYTOKERATIN 8, CYTOTOXICT-LYMPHOCYTE-ASSOCIATED SERINE ESTERASE 8 (CTLA-8),DIFFERENTIATION-INHIBITING ACTIVITY (DIA), DNA AMPLIFIED IN MAMMARYCARCINOMA 1 (DAM1), DNA TOPOISOMERASE II ALPHA, DR-NM23, E-CADHERIN,EMMPRIN, EMS1, ENDOTHELIAL CELL GROWTH FACTOR (ECGR), PLATELET-DERIVED(PD-ECGF), ENKEPHALINASE, EPIDERMAL GROWTH FACTOR RECEPTOR (EGFR),EPISIALIN, EPITHELIAL MEMBRANE ANTIGEN (EMA), ER-ALPHA, ERBB2, ERBB4,ER-BETA, ERF-1, ERYTHROID-POTENTIATING ACTIVITY (EPA), ESR1, ESTROGENRECEPTOR-ALPHA, ESTROGEN RECEPTOR-BETA, ETS-1, EXTRACELLULAR MATRIXMETALLOPROTEINASE INDUCER (EMMPRIN), FIBRONECTIN RECEPTOR, BETAPOLYPEPTIDE (FNRB), FIBRONECTIN RECEPTOR BETA SUBUNIT (FNRB), FLK-1,GA15.3, GA733.2, GALECTIN-3, GAMMA-CATENIN, GAP JUNCTION PROTEIN (26kDa), GAP JUNCTION PROTEIN (43 kDa), GAP JUNCTION PROTEIN ALPHA-1(GJA1), GAP JUNCTION PROTEIN BETA-2 (GJB2), GCP1, GELATINASE A,GELATINASE B, GELATINASE (72 kDa), GELATINASE (92 kDa), GLIOSTATIN,GLUCOCORTICOID RECEPTOR INTERACTING PROTEIN 1 (GRIP1), GLUTATHIONES-TRANSFERASE p, GM-CSF, GRANULOCYTE CHEMOTACTIC PROTEIN 1 (GCP1),GRANULOCYTE-MACROPHAGE-COLONY STIMULATING FACTOR, GROWTH FACTOR RECEPTORBOUND-7 (GRB-7), GSTp, HAP, HEAT-SHOCK COGNATE PROTEIN 70 (HSC70),HEAT-STABLE ANTIGEN, HEPATOCYTE GROWTH FACTOR (HGF), HEPATOCYTE GROWTHFACTOR RECEPTOR (HGFR), HEPATOCYTE-STIMULATING FACTOR III (HSF III),HER-2, HER2/NEU, HERMES ANTIGEN, HET, HHM, HUMORAL HYPERCALCEMIA OFMALIGNANCY (HHM), ICERE-1, INT-1, INTERCELLULAR ADHESION MOLECULE-1(ICAM-1), INTERFERON-GAMMA-INDUCING FACTOR (IGIF), INTERLEUKIN-1 ALPHA(IL-1A), INTERLEUKIN-1 BETA (IL-1B), INTERLEUKIN-11 (IL-11),INTERLEUKIN-17 (IL-17), INTERLEUKIN-18 (IL-18), INTERLEUKIN-6 (IL-6),INTERLEUKIN-8 (IL-8), INVERSELY CORRELATED WITH ESTROGEN RECEPTOREXPRESSION-1 (ICERE-1), KAI1, KDR, KERATIN 8, KERATIN 18, KERATIN 19,KISS-1, LEUKEMIA INHIBITORY FACTOR (LIF), LIF, LOST IN INFLAMMATORYBREAST CANCER (LIBC), LOT (“LOST ON TRANSFORMATION”), LYMPHOCYTE HOMINGRECEPTOR, MACROPHAGE-COLONY STIMULATING FACTOR, MAGE-3, MAMMAGLOBIN,MASPIN, MC56, M-CSF, MDC, MDNCF, MDR, MELANOMA CELL ADHESION MOLECULE(MCAM), MEMBRANE METALLOENDOPEPTIDASE (MME), MEMBRANE-ASSOCIATED NEUTRALENDOPEPTIDASE (NEP), CYSTEINE-RICH PROTEIN (MDC), METASTASIN (MTS-1),MLN64, MMP1, MMP2, MMP3, MMP7, MMP9, MMP11, MMP13, MMP14, MMP15, MMP16,MMP17, MOESIN, MONOCYTE ARGININE-SERPIN, MONOCYTE-DERIVED NEUTROPHILCHEMOTACTIC FACTOR, MONOCYTE-DERIVED PLASMINOGEN ACTIVATOR INHIBITOR,MTS-1, MUC-1, MUC18, MUCIN LIKE CANCER ASSOCIATED ANTIGEN (MCA), MUCIN,MUC-1, MULTIDRUG RESISTANCE PROTEIN 1 (MDR, MDR1), MULTIDRUG RESISTANCERELATED PROTEIN-1 (MRP, MRP-1), N-CADHERIN, NEP, NEU, NEUTRALENDOPEPTIDASE, NEUTROPHIL-ACTIVATING PEPTIDE 1 (NAP1), NM23-H1, NM23-H2,NME1, NME2, NUCLEAR RECEPTOR COACTIVATOR-1 (NCoA-1), NUCLEAR RECEPTORCOACTIVATOR-2 (NCoA-2), NUCLEAR RECEPTOR COACTIVATOR-3 (NCoA-3),NUCLEOSIDE DIPHOSPHATE KINASE A (NDPKA), NUCLEOSIDE DIPHOSPHATE KINASE B(NDPKB), ONCOSTATIN M (OSM), ORNITHINE DECARBOXYLASE (ODC), OSTEOCLASTDIFFERENTIATION FACTOR (ODF), OSTEOCLAST DIFFERENTIATION FACTOR RECEPTOR(ODFR), OSTEONECTIN (OSN, ON), OSTEOPONTIN (OPN), OXYTOCIN RECEPTOR(OXTR), p27/kip1, p300/CBP COINTEGRATOR ASSOCIATE PROTEIN (p/CIP), p53,p9Ka, PAI-1, PAI-2, PARATHYROID ADENOMATOSIS 1 (PRAD1), PARATHYROIDHORMONE-LIKE HORMONE (PTHLH), PARATHYROID HORMONE-RELATED PEPTIDE(PTHrP), P-CADHERIN, PD-ECGF, PDGF, PEANUT-REACTIVE URINARY MUCIN (PUM),P-GLYCOPROTEIN (P-GP), PGP-1, PHGS-2, PHS-2, PIP, PLAKOGLOBIN,PLASMINOGEN ACTIVATOR INHIBITOR (TYPE 1), PLASMINOGEN ACTIVATORINHIBITOR (TYPE 2), PLASMINOGEN ACTIVATOR (TISSUE-TYPE), PLASMINOGENACTIVATOR (UROKINASE-TYPE), PLATELET GLYCOPROTEIN IIIa (GP3A), PLAU,PLEOMORPHIC ADENOMA GENE-LIKE 1 (PLAGL1), POLYMORPHIC EPITHELIAL MUCIN(PEM), PRAD1, PROGESTERONE RECEPTOR (PgR), PROGESTERONE RESISTANCE,PROSTAGLANDIN ENDOPEROXIDE SYNTHASE-2, PROSTAGLANDIN G/H SYNTHASE-2,PROSTAGLANDIN H SYNTHASE-2, pS2, PS6K, PSORIASIN, PTHLH, PTHrP, RAD51,RAD52, RAD54, RAP46, RECEPTOR-ASSOCIATED COACTIVATOR 3 (RAC3), REPRESSOROF ESTROGEN RECEPTOR ACTIVITY (REA), S100A4, S100A6, S100A7, S6K,SART-1, SCAFFOLD ATTACHMENT FACTOR B (SAF-B), SCATTER FACTOR(SF),SECRETED PHOSPHOPROTEIN-1 (SPP-1), SECRETED PROTEIN, ACIDIC AND RICH INCYSTEINE (SPARC), STANNICALCIN, STEROID RECEPTOR COACTIVATOR-1 (SRC-1),STEROID RECEPTOR COACTIVATOR-2 (SRC-2), STEROID RECEPTOR COACTIVATOR-3(SRC-3), STEROID RECEPTOR RNA ACTIVATOR(SRA), STROMELYSIN-1,STROMELYSIN-3, TENASCIN-C (TN-C), TESTES-SPECIFIC PROTEASE 50,THROMBOSPONDIN I, THROMBOSPONDIN II, THYMIDINE PHOSPHORYLASE (TP),THYROID HORMONE RECEPTOR ACTIVATOR MOLECULE 1 (TRAM-1), TIGHT JUNCTIONPROTEIN 1 (TJP1), TIMP1, TIMP2, TIMP3, TIMP4, TISSUE FACTOR (TF),TISSUE-TYPE PLASMINOGEN ACTIVATOR, TN-C, TP53, tPA, TRANSCRIPTIONALINTERMEDIARY FACTOR 2 (TIF2), TREFOIL FACTOR 1 (TFF1), TSG101, TSP-1,TSP1, TSP-2, TSP2, TSP50, TUMOR CELL COLLAGENASE STIMULATING FACTOR(TCSF), TUMOR-ASSOCIATED EPITHELIAL MUCIN, uPA, uPAR, UROKINASE,UROKINASE-TYPE PLASMINOGEN ACTIVATOR, UROKINASE-TYPE PLASMINOGENACTIVATOR RECEPTOR (uPAR), UVOMORULIN, VASCULAR ENDOTHELIAL GROWTHFACTOR, VASCULAR ENDOTHELIAL GROWTH FACTOR RECEPTOR-2 (VEGFR2), VASCULARENDOTHELIAL GROWTH FACTOR-A, VASCULAR PERMEABILITY FACTOR, VEGFR2, VERYLATE T-CELL ANTIGEN BETA (VLA-BETA), VIMENTIN, VITRONECTIN RECEPTORALPHA POLYPEPTIDE (VNRA), VITRONECTIN RECEPTOR, VON WILLEBRAND FACTOR,VPF, VWF, WNT-1, ZAC, ZO-1, and ZONULA OCCLUDENS-1. The genes and/orgene products can be part of a biosignature for theranosing a cancer.

As an illustration, a treatment can be selected for a subject sufferingfrom Non-Small Cell Lung Cancer. One or more biomarkers, such as, butnot limited to, EGFR, excision repair cross-complementation group 1(ERCC1), p53, Ras, p27, class III beta tubulin, breast cancer gene 1(BRCA1), breast cancer gene 1 (BRCA2), and ribonucleotide reductasemessenger 1 (RRM1), can be assessed from a vesicle from the subject.Based on one or more characteristics of the one or more biomarkers, thesubject can be determined to be a responder or non-responder for atreatment, such as, but not limited to, Erlotinib, Carboplatin,Paclitaxel, Gefitinib, or a combination thereof.

In another embodiment, a treatment can be selected for a subjectsuffering from Colorectal Cancer, and a biomarker, such as, but notlimited to, K-ras, can be assessed from a vesicle from the subject.Based on one or more characteristics of the one or more biomarkers, thesubject can be determined to be a responder or non-responder for atreatment, such as, but not limited to, Panitumumab, Cetuximab, or acombination thereof.

In another embodiment, a treatment can be selected for a subjectsuffering from Breast Cancer. One or more biomarkers, such as, but notlimited to, HER2, toposiomerase II α, estrogen receptor, andprogesterone receptor, can be assessed from a vesicle from the subject.Based on one or more characteristics of the one or more biomarkers, thesubject can be determined to be a responder or non-responder for atreatment, such as, but not limited to, trastuzumab, anthracyclines,taxane, methotrexate, fluorouracil, or a combination thereof.

As described, the biosignature used to theranose a cancer can compriseanalysis of one or more biomarker, which can be a protein or nucleicacid, including a mRNA or a microRNA. The biomarker can be detected in abodily fluid and/or can be detected associated with a vesicle, e.g., asa vesicle antigen or as vesicle payload. In an illustrative example, thebiosignature is used to identify a patient as a responder ornon-responder to a tyrosine kinase inhibitor. The biomarkers can be oneor more of those described in WO/2010/121238, entitled “METHODS AND KITSTO PREDICT THERAPEUTIC OUTCOME OF TYROSINE KINASE INHIBITORS” and filedApr. 19, 2010; or WO/2009/105223, entitled “SYSTEMS AND METHODS OFCANCER STAGING AND TREATMENT” and filed Feb. 19, 2009; both of whichapplications are incorporated herein by reference in their entirety.

In an aspect, the present invention provides a method of determiningwhether a subject is likely to respond or not to a tyrosine kinaseinhibitor, the method comprising identifying one or more biomarker in avesicle population in a sample from the subject, wherein differentialexpression of the one or more biomarker in the sample as compared to areference indicates that the subject is a responder or non-responder tothe tyrosine kinase inhibitor. In an embodiment, the one or morebiomarker comprises miR-497, wherein reduced expression of miR-497indicates that the subject is a responder (i.e., sensitive to thetyrosine kinase inhibitor). In another embodiment, the one or morebiomarker comprises one or more of miR-21, miR-23a, miR-23b, andmiR-29b, wherein upregulation of the microRNA indicates that the subjectis a likely non-responder (i.e., resistant to the tyrosine kinaseinhibitor). In some embodiments, the one or more biomarker comprises oneor more of hsa-miR-029a, hsa-let-7d, hsa-miR-100, hsa-miR-1260,hsa-miR-025, hsa-let-7i, hsa-miR-146a, hsa-miR-594-Pre, hsa-miR-024,FGFR1, MET, RAB25, EGFR, KIT and VEGFR2. In another embodiment, the oneor more biomarker comprises FGF1, HOXC10 or LHFP, wherein higherexpression of the biomarker indicates that the subject is anon-responder (i.e., resistant to the tyrosine kinase inhibitor). Themethod can be used to determine the sensitivity of a cancer to thetyrosine kinase inhibitor, e.g., a non-small cell lung cancer cell,kidney cancer or GIST. The tyrosine kinase inhibitor can be erlotinib,vandetanib, sunitinib and/or sorafenib, or other inhibitors that operateby a similar mechanism of action. A tyrosine kinase inhibitor includesany agent that inhibits the action of one or more tyrosine kinases in aspecific or non-specific fashion. Tyrosine kinase inhibitors includesmall molecules, antibodies, peptides, or any appropriate entity thatdirectly, indirectly, allosterically, or in any other way inhibitstyrosine residue phosphorylation. Specific examples of tyrosine kinaseinhibitors includeN-(trifluoromethylphenyl)-5-methylisoxazol-4-carboxamide,3-[(2,4-dimethylpyrrol-5-yl)methylidenyl)indolin-2-one,17-(allylamino)-17-demethoxygeldanamycin,4-(3-chloro-4-fluorophenylamino)-7-methoxy-6-[3-(4-morpholinyl)propoxyl]q-uinazoline,N-(3-ethynylphenyl)-6,7-bis(2-methoxyethoxy)-4-quinazolinamine,BIBX1382,2,3,9,10,11,12-hexahydro-10-(hydroxymethyl)-10-hydroxy-9-methyl-9,12-epox-y-1H-d{umlautover(υ)}ndolo[1,2,3-fg:3′,2′,1′-k1]pyrrolo[3,4-i][1,6]benzodiazocin-1-one,SH268, genistein, STI571, CEP2563,4-(3-chlorophenylamino)-5,6-dimethyl-7H-pyrrolo[2,3-d]pyrimidinemethanesulfonate, 4-(3-bromo-4-hydroxyphenyl)amino-6,7-dimethoxyquinazoline,4-(4′-hydroxyphenyl)amino-6,7-dimethoxyquinazoline, SU6668, STI571A,N-4-chlorophenyl-4-(4-pyridylmethyl)-1-phthalazinamine,N-[2-(diethylamino)ethyl]-5-[(Z)-(5-fluoro-1,2-dihydro-2-oxo-3H-indol-3-ylidine)methyl]-2,4-dimethyl-1H-pyrrole-3-carboxamide(commonly known as sunitinib), A-[A-[[4-chloro-3(trifluoromethyl)phenyl]carbamoylamino]phenoxy]-N-methyl-pyridine-2-carboxamide (commonly knownas sorafenib), EMD121974, andN-(3-ethynylphenyl)-6,7-bis(2-methoxyethoxy)quinazolin-4-amine (commonlyknown as erlotinib). In some embodiments, the tyrosine kinase inhibitorhas inhibitory activity upon the epidermal growth factor receptor(EGFR), VEGFR, PDGFR beta, and/or FLT3.

Thus, a treatment can be selected for the subject suffering from acancer, based on a biosignature identified by the methods of theinvention. Accordingly, the biosignature can comprise a presence orlevel of a circulating biomarker, including a microRNA, a vesicle, orany useful vesicle associated biomarker.

Cardiovascular

Assessing a vesicle can be used in the theranosis of a cardiovascularcondition, disorder, or disease. A cardiovascular condition includes,but is not limited to, chronic rheumatic heart disease, hypertensivedisease, ischemic heart disease, pulmonary circulatory disease, heartdisease, cerebrovascular disease, diseases of arteries, arterioles andcapillaries and diseases of veins and lymphatics. A chronic rheumaticheart disease includes, but is not limited to diseases of mitral valve,diseases of aortic valve, diseases of mitral and aortic valves, anddiseases of other endocardial structures. A hypertensive diseaseincludes, but is not limited to essential hypertension, hypertension,malignant, hypertension, benign, hypertension, unspecified, hypertensiveheart disease, hypertensive renal disease, hypertensive renal disease,unspecified, with renal failure, hypertensive heart and renal disease,hypertension, renovascular, malignant, and hypertension, renovascularbenign. An ischemic heart disease includes, but is not limited to acutemyocardial infarction, myocardiac infarction, acute, anterolateral,myocardiac infarction, acute, anterior, myocardiac infarction, acute,inferolateral, myocardiac infarction, acute, inferoposterior, myocardiacinfarction, acute, other inferior wall, myocardiac infarction, acute,other lateral wall, myocardiac infarction, acute, true posterior,myocardiac infarction, acute, subendocardial, myocardiac infarction,acute, spec, myocardiac infarction, acute, unspecified, postmyocardialinfarction syndrome, intermediate coronary syndrome, old myocardialinfarction, angina pectoris, angina decubitus, prinzmetal angina,coronary atherosclerosis, aneurysm and dissection of heart, aneurysm ofheart wall, aneurysm of coronary vessels, dissection of coronary artery,and unspecified chronic ischemic heart disease.

A pulmonary circulatory disease includes, but is not limited to,diseases of pulmonary circulation, acute pulmonary heart disease,pulmonary embolism, not iatrogenic, chronic pulmonary heart disease, andunspecified chronic pulmonary heart disease. A heart disease includes,but is not limited to acute pericarditis, other and unspecified acutepericarditis, acute nonspecific pericarditis, acute and subacuteendocarditis, acute bacterial endocarditis acute myocarditis, other andunspecified acute myocarditis, myocarditis, idiopathic, other diseasesof pericardium, other diseases of endocardium, alvular disorder, mitral,valvular disorder, aortic, valvular disorder, tricuspid, valvulardisorder, pulmonic, cardiomyopathy, hypertrophic obstructivecardiomyopathy, conduction disorders, atrioventricular block, thirddegree, atrioventricular block, first degree, atrioventricular block,mobitz ii, atrioventricular block, wenckebach's, bundle branch block,left, bundle branch block, right, sinoatrial heart block,atrioventricular excitation, anomalous, Wolff Parkinson White syndrome,cardiac dysrhythmias, tachycardia, paroxysmal supraventricular, atrialfibrillation and flutter, atrial fibrillation, atrial flutter,ventricular fibrillation and flutter, ventricular fibrillation, cardiacarrest, premature beats, other specified cardiac dysrhythmias, sicksinus syndrome, sinus bradycardia, cardiac dysrhythmia unspecified,gallop rhythm, heart failure, heart failure, congestive, acute pulmonaryedema, systolic unspecified heart failure, acute systolic heart failure,chronic systolic heart failure, diastolic unspecified heart failure,diastolic chronic heart failure, combined unspecified heart failure, andcardiomegaly.

A cerebrovascular disease includes, but is not limited to subarachnoidhemorrhage, intracerebral hemorrhage, other and unspecified intracranialhemorrhage, intracranial hemorrhage, occlusion and stenosis ofprecerebral arteries, occlusion and stenosis of basilar artery,occlusion and stenosis of carotid artery, occlusion and stenosis ofvertebral artery, occlusion of cerebral arteries, cerebral thrombosis,cerebral thrombosis without cerebral infarction, cerebral thrombosiswith cerebral infarction, cerebral embolism, cerebral embolism withoutcerebral infarction, cerebral embolism with cerebral infarction,transient cerebral ischemia, basilar artery syndrome, vertebral arterysyndrome, subclavian steal syndrome, vertebrobasilar artery syndrome,transient ischemic attack, acute but ill defined cerebrovasculardisease, ill defined cerebrovascular disease, cerebral atherosclerosis,other generalized ischemic cerebrovascular disease, hypertensiveencephalopathy, cerebral aneurysm nonruptured, cerebral arteritis,moyamoya disease, nonpyogenic thrombosis of intracranial venous sinus,transient global amnesia, late effects of cerebrovascular disease,cognitive deficits, speech and language deficits, unspecified speech andlanguage deficits, aphasia, dysphasia, other speech and languagedeficits, hemiplegia/hemiparesis, hemiplegia affecting unspecified side,hemiplegia affecting dominant side, hemiplegia affecting nondominantside, monoplegia of upper limb, monoplegia of lower limb, otherparalytic syndrome, other late effects of cerebrovascular disease,apraxia cerebrovascular disease, dysphagia cerebrovascular disease,facial weakness, ataxia, and vertigo.

Diseases of arteries, arterioles and capillaries include, but are notlimited to atherosclerosis, atherosclerosis of renal artery,atherosclerosis of native arteries of the extremities, intermittentclaudication, atherosclerosis, extremities, without ulceration,atherosclerosis, not heart/brain, aortic aneurysm, dissection of aorta,abdominal ruptured aortic aneurysm, abdominal, without ruptured aorticaneurysm, unspecified aortic aneurysm, other aneurysm, other peripheralvascular disease, raynaud's syndrome, thromboangiitis obliterans, otherarterial dissection, dissection of carotid artery, dissection of iliacartery, dissection of renal artery, dissection of vertebral artery,dissection of other artery, erythromelalgia, unspecified peripheralvascular disease, arterial embolism and thrombosis, polyarteritis nodosaand allied conditions, polyarteritis nodosa, kawasaki disease/acutefebrile mucocutaneous lymph node syndrome, hypersensitivity angiitis,goodpasture's syndrome, lethal midline granuloma, wegener'sgranulomatosis, giant cell arteritis, thrombotic microangiopathy,takayasu's disease, other disorders of arteries and arterioles,arteriovenous fistula acquired, arteritis unspecified, vasculitis, andvascular non-neoplastic nevus.

Diseases of veins and lymphatics include, but are not limited to,phlebitis and thrombophlebitis, femoral deep vein thrombosis, deep veinthrombosis of other leg veins, phlebitis of other sites, superficialveins of upper extremity, unspecified thrombophlebitis, portal veinthrombosis, other venous embolism and thrombosis, unspecified deep veinthrombosis, proximal deep vein thrombosis, distal deep vein thrombosis,unspecified venous embolism, varicose veins of lower extremities,varicose veins without ulcer, varicose veins without inflammation,varicose veins withoutulcer, inflammation, varicose veins, asymptomatic,hemorrhoids, hemorrhoids, internal without complication, hemorrhoids,external without complication, hemorrhoids, external thrombosed,hemorrhoids, varicose veins of other sites, esophageal varices withoutbleeding, esophageal varices without bleeding, varicocele, noninfectivedisorders of lymphatic channels, postmastectomy lymphedema syndrome,hypotension, orthostatic hypotension, iatrogenic hypotension, otherdisorders of circulatory system, other specified disorders ofcirculatory system, and unspecified venous insufficiency.

Other examples of cardiac conditions include, without limitation,coronary artery occlusion (e.g., resulting from or associated withlipid/cholesterol deposition, macrophage/inflammatory cell recruitment,plaque rupture, thrombosis, platelet deposition, or neointimalproliferation); ischemic syndromes (e.g., resulting from or associatedwith myocardial infarction, stable angina, unstable angina, coronaryartery restenosis or reperfusion injury); cardiomyopathy (e.g.,resulting from or associated with an ischemic syndrome, a cardiotoxin,an infection, hypertension, a metabolic disease (such as uremia,beriberi, or glycogen storage disease), radiation, a neuromusculardisease, an infiltrative disease (such as sarcoidosis, hemochromatosis,amyloidosis, Fabry's disease, or Hurler's syndrome), trauma, or anidiopathic cause); arrhythmia or dysrrhythmia (e.g., resulting from orassociated with an ischemic syndrome, a cardiotoxin, adriamycin, aninfection, hypertension, a metabolic disease, radiation, a neuromusculardisease, an infiltrative disease, trauma, or an idiopathic cause);infection (e.g., caused by a pathogenic agent such as a bacterium, avirus, a fungus, or a parasite); and an inflammatory condition (e.g.,associated with myocarditis, pericarditis, endocarditis, immune cardiacrejection, or an inflammatory conditions resulting from one ofidiopathic, autoimmune, or a connective tissue disease).

Cardiovascular: Biosignature

A biosignature of a vesicle can be assessed to provide a theranosis fora subject. The biosignature of the vesicle can comprise one or morebiomarkers such as, but not limited to, any one or more biomarkers asdescribed herein, such as, but not limited to, those listed in FIG. 24,miR-21, miR-129, miR-212, miR-214, miR-134, and others such as describedin US Publication No. 2010/0010073.

Cardiovascular: Standard of Care

Determining the biosignature of a vesicle, the amount of vesicles, orboth, of a sample from a subject suffering from a cardiac condition,disorder, or disease, can be used select a standard of care for thesubject. The standard of care may include therapeutic agents orprocedures (e.g., angioplasty). Examples of therapeutic agents include,without limitation, angiogenesis promoters (e.g., vascular endothelialgrowth factor, nitric oxide releasing or generating agents, fibroblastgrowth factor, platelet derived growth factor, interleukin-6, monocytechemotactic protein-1, granulocyte-macrophage colony stimulating factor,transforming growth factor-.beta.), anti-thrombotic agents (e.g.,aspirin, heparin, PPACK, enoxaprin, hirudin), anticoagulants,antibiotics, antiplatelet agents, thrombolytics (e.g., tissueplasminogen activator), antiproliferatives, antiinflammatories, agentsthat inhibit hyperplasia, agents that inhibit restenosis, smooth musclecell inhibitors, growth factors, growth factor inhibitors, cell adhesioninhibitors, chemotherapeutic agents, and combinations thereof.

For example, detection of one or more microRNAs biomarkers, such asmiR-21, miR-129, miR-212, miR-214, miR-134 or a combination thereof fromvesicles can be used to characterize a cardiac hypertrophy and/or heartfailure, which provides a theranosis for the cardiac hypertrophy. Thetheranosis can include selecting a therapy such as adminsteringangiogenesis promoters. Other examples of treatments include those fortreating abnormal cholesterol and/or triglyceride levels in the blood,such as listed in Table 12.

TABLE 12 Examples of Classes of Drugs for Treatment of CardiovascularConditions Class Mechanism of Action Examples Statins Competitiveinhibitors of HMG-CoA reductase Atorvastatin, Simvastatin, Pravastatin,Fluvastatin, Rosuvastatin, Lovastatin, Pitavastatin, Cerivastatin(withdrawn) Fibrates PPARα activators Fenofibrate, Bezafibrate,Gemfibrozil, clofibrate, ciprofibrate Cholesterol May inhibit NCP1L1 ingut Ezetimibe Absorption Inhibitors Nicotinic Inhibits cholesterol andtriglyceride synthesis, exact mechanism Niacin Acid unknown DerivativesBile Acid Interrupt the enterohepatic circulation of bile acidsColesevelam, Sequestrants Cholestyramine, Colestimide, ColestipolCholesteryl Inhibit cholesteryl ester transfer protein, a plasma proteinthat JTT-705, CETi-1, Ester Transfer mediates the exchange ofcholesteryl esters from antiatherogenic Torcetrapib Protein HDL toproatherogenic apoliprotein B-containing lipoproteins Inhibitors ReverseLipid Stimulate reverse lipid transport, a four-step process formETC-216, ETC-588, ETC- Transport removing excess cholesterol and otherlipids from the walls of 642, ETC-1001, ESP-1552, Pathway arteries andother tissues ESP-24232 Activators Antioxidants/ Inhibit vascularinflammation and reduce cholesterol levels; AGI-1067, Probucol Vascularblock oxidant signals that switch on vascular cellular adhesion(withdrawn) Protectants molecule (VCAM)-1 Acyl-CoA Inhibit ACAT, whichcatalyzes cholesterol esterification, Eflucimibe, Pactimibe, Cholesterolregulates intracellular free cholesterol, and promotes cholesterolAvasimibe (withdrawn), Acyltransferase absorption and assemble of VLDLSMP-797 (ACAT) Inhibitors Peroxisome Activate PPARs, e.g., PPARα, γ, andpossibly δ, which have a Tesaglitazar, GW-50516, Proliferator variety ofgene regulatory functions GW-590735, LY-929, LY- Activated 518674,LY-465608, LY- Receptor 818 Agonists Microsomal Inhibit MTTP, whichcatalyze the transport of triglycerides, Implitapide, CP-346086Triglyceride cholesteryl ester, and phosphatidylcholine betweenmembranes; Transfer required for the synthesis of ApoB. Protein (MTTP)Inhibitors Squalene Interfere with cholesterol synthesis by halting theaction of liver TAK-475, ER-119884 Synthase enzymes; may also slow orstop the proliferation of several cell Inhibitors types that contributeto atherosclerotic plaque formation Lipoprotein Directly activatelipoprotein lipase, which promotes the Ibrolipim (NO-1886) Lipasebreakdown of the fat portion of lipoproteins Activators Liproprotein Notyet established Gembacene (a) Antagonists Bile Acid Inhibit intestinalepithelial uptake of bile acids. AZD-7806, BARI-1453, S- Reabsorption8921 Inhibitors

In one embodiment, a treatment can be selected for a subject sufferingfrom Peripheral Arterial Disease. One or more biomarkers, such as, butnot limited to, C-reactive protein (CRP), serum Amylyoid A (SAA),interleukin-6, intracellular adhesion molecule (ICAM), vascular adhesionmolecule (VCAM), CD40L, fibrinogen, fibrin D-dimer, fibrinopeptide A,von Willibrand factor, tissue plasminogen activator antigen (t-PA),factor VII, prothrombin fragment 1, oxidized low density lipoprotein(oxLDL), and lipoprotein A, can be assessed from a vesicle from thesubject. Based on one or more characteristics of the one or morebiomarkers, the subject can be determined to be a responder ornon-responder for a treatment, such as, but not limited to,Atorvastatin, Simvastatin, Rosuvastatin, Pravastatin, Fluvastatin,Lovastatin, or a combination thereof.

In another embodiment, a treatment can be selected for a subjectsuffering from an arrhythmia. One or more biomarkers, such as, but notlimited to, SERCA, AAP, Connexin 40, Connexin 43, ATP-sensitivepotassium channel, Kv1.5 channel, and acetylcholine-activated posassiumchannel, can be assessed from a vesicle from the subject. Based on oneor more characteristics of the one or more biomarkers, the subject canbe determined to be a responder or non-responder for a treatment, suchas, but not limited to, Disopyramide, Flecamide, Lidocaine, Mexiletine,Moricizine, Procainamide, Propafenone, Quinidine, Tocamide, Acebutolol,Atenolol, Betaxolol, Bisoprolol, Carvedilol, Esmolol, Metoprolol,Nadolol, Propranolol, Sotalol, Timolol, Amiodarone, Azimilide, Bepridil,Dofetilide, Ibutilide, Tedisamil, Diltiazem, Verapamil, Azimilide,Dronedarone, Amiodarone, PM101, ATI-2042, Tedisamil, Nifekalant,Ambasilide, Ersentilide, Trecetilide, Almokalant, D-sotalol, BRL-32872,HMR1556, L768673, Vernakalant, AZD70009, AVE0118, 59947, NIP-141/142,XEN-D0101/2, Ranolazine, Pilsicamide, JTV519, Rotigaptide, GAP-134, or acombination thereof.

In another embodiment, a treatment can be selected for a subjectsuffering from abnormal coagulation. One or more biomarkers, such as,but not limited to, F 1.2, TAT, FPA, beta-throboglobulin, plateletfactor 4, soluble P-selectin, IL-6, and CRP can be assessed from avesicle from the subject. Based on one or more characteristics of theone or more biomarkers, the subject can be determined to be a responderor non-responder for a treatment, such as, but not limited to, aspirin,anticoagulants, ximelagatran, Heparin, Warfarin, or a combinationthereof.

In another embodiment, a treatment can be selected for a subjectsuffering from Premature Atherosclerosis. One or more biomarkers, suchas, but not limited to, CRP, NF-kB, IL-1, IL-6, IL-18, Apo-B, Lp-PLA2,Fibrinogen, Hcy, and Hcy-thiolactone can be assessed from a vesicle fromthe subject. Based on one or more characteristics of the one or morebiomarkers, the subject can be determined to be a responder ornon-responder for a treatment.

In yet another embodiment, a treatment can be selected for a subjectsuffering from Hypertension. One or more biomarkers, such as, but notlimited to, Brain natriuretic peptide and N-terminal prohormone BNP, canbe assessed from a vesicle from the subject. Based on one or morecharacteristics of the one or more biomarkers, the subject can bedetermined to be a responder or non-responder for a treatment.

In another embodiment, a treatment can be selected for a subjectsuffering from Cardiovascular Disease. One or more biomarkers, such as,but not limited to, an ACE inhibitor or angiotensin can be assessed froma vesicle from the subject. Based on one or more characteristics of theone or more biomarkers, the subject can be determined to be a responderor non-responder for a treatment, such as, but not limited to,lisinopril, candesartan, enalapril, or a combination thereof.

Thus, a treatment can be selected for the subject suffering from acardiology related condition or cardiovascular condition, based on thebiosignature of the subject's vesicle.

Autoimmune

Assessing a vesicle can be used in the theranosis of an autoimmunecondition, disorder, or disease. Autoimmune conditions are conditionswhere a mammal's immune system starts reacting against its own tissues.Such conditions include, without limitation, systemic lupuserythematosus (SLE), discoid lupus, lupus nephritis, sarcoidosis,inflammatory arthritis, including juvenile arthritis, rheumatoidarthritis, psoriatic arthritis, Reiter's syndrome, ankylosingspondylitis, and gouty arthritis, multiple sclerosis, hyper IgEsyndrome, polyarteritis nodosa, primary biliary cirrhosis, inflammatorybowel disease, Crohn's disease, celiac's disease (gluten-sensitiveenteropathy), autoimmune hepatitis, pernicious anemia, autoimmunehemolytic anemia, psoriasis, scleroderma, myasthenia gravis, autoimmunethrombocytopenic purpura, autoimmune thyroiditis, Grave's disease,Hasimoto's thyroiditis, immune complex disease, chronic fatigue immunedysfunction syndrome (CFIDS), polymyositis and dermatomyositis,cryoglobulinemia, thrombolysis, cardiomyopathy, pemphigus vulgaris,pulmonary interstitial fibrosis, asthma, Churg-Strauss syndrome(allergic granulomatosis), atopic dermatitis, allergic and irritantcontact dermatitis, urtecaria, IgE-mediated allergy, atherosclerosis,vasculitis, idiopathic inflammatory myopathies, hemolytic disease,Alzheimer's disease, chronic inflammatory demyelinating polyneuropathy,chagas disease, chronic obstruct pulmonary disease, dermatomyositis,diabetes mellitus type 1, endometriosis, goodpasture's syndrome, graves'disease, guillain-barre syndrome (gbs), Hashimoto's disease,hidradenitis suppurat a, kawasaki disease, iga nephropathy, idiopathicthrombocytopenic purpura, interstitial cystitis, lupus erythematosus i,mixed connect e tissue disease, morphea, myasthenia gravis, narcolepsy,neuromyotonia, pemphigus vulgaris, pernicious anaemia, psoriasis,psoriatic arthritis, polymyositis, primary biliary cirrhosis, rheumatoidarthritis, schizophrenia, scleroderma, sjögren's syndrome, stiff personsyndrome, temporal arteritis, ulcerat e colitis, vasculitis, vitiligo,Wegener's granulomatosis, and AID.

Autoimmune: Biosignature

A biosignature of a vesicle can be assessed to provide a theranosis fora subject. The biosignature of the vesicle can comprise one or morebiomarkers such as, but not limited to, a biomarker such as listed inFIG. 1 for autoimmune disease, or for other autoimmune diseases, suchas, but not limited to those listed in FIGS. 23, 34, 35, 36, 39, 41, 42,and 56.

Autoimmune: Standard of Care

Determining the biosignature of a vesicle, the amount of vesicles, orboth, of a sample from a subject suffering from an autoimmune condition,disorder or disease can be used to select a standard of care for thesubject. Most autoimmune diseases cannot yet be treated directly, butare treated according to symptoms associated with the condition. Thestandard of care includes, for example, prescribing corticosteroiddrugs, non-steroidal anti-inflammatory drugs (NTHEs) or more powerfulimmunosuppressant drugs such as cyclophosphamide, methotrexate andazathioprine that suppress the immune response and stop the progressionof the disease. Radiation of the lymph nodes and plasmapheresis (aprocedure that removes the diseased cells and harmful molecules from theblood circulation) are other ways of treating an autoimmune disease.

Examples of drugs or agents for use in treating autoimmune diseases,which can be selected based on a profiling of a vesicle from a subject,include those in Table 13 for a subject suffering from diabetes, inTable 14 for those suffering from Multiple Sclerosis.

TABLE 13 Example of Classes of Drugs for Treatment of Diabetes ClassMechanism of Action Examples Peroxisome Target PPAR-gamma or PPAR-gammaand -alpha (see below). Rosiglitazone, Pioglitazone, Proliferator- PPARare nuclear receptors that help regulate glucose and lipidBalaglitazone, see also Activated metabolism. Activation of PPAR-gammaimproves insulin others described herein Receptor sensitivity and thusimproves glycemic control. (PPAR) Agonists Dual-Action Act on bothPPAR-gamma and PPAR-alpha. PPAR-alpha TAK-559, Muraglitazar, Peroxisomeactivation has effects on cellular uptake of fatty acids and theirTesaglitazar, Netoglitazone, Proliferator- oxidation, and on lipoproteinmetabolism. May also act to reduce see also others described Activatedinflammatory response in vascular endothelial cells. herein ReceptorAgonists Biguanidines Complete mechanism is not known. Reducesgluconeogenesis in Metformin, Metformin GR the liver by inhibitingglucose-6-phosphatase. Sulfonylureas Induce insulin secretion by bindingto cellular receptors that Glimepride, cause membrane depolarization andinsulin exocytosis. Glyburide/glibenclamide, Glipizide, Gliclazide.Tobutamide Insulin and Supplements endogenous insulin. Insulin analogshave a variety Insulin lispro, Insulin aspart, Insulin of amino acidchanges and have altered onset of action and Insulin glargine, Exubera,Analogs duration of action, as well as other properties, compared tonative AERx Insulin Diabetes (Injectable, insulin. Inhaled insulin isabsorbed through the alveoli. Spray oral Management System, HIM-Inhaled, Oral, insulin is absorbed by the buccal mucosa and intranasalthrough 2, Oaralin, Insulin detemir, Transdermal, the nasal mucosa.Transdermal insulin is absorbed through the Insulin glulisineIntranasal) skin. Meglitinides Are thought to bind to a nonsulfonylureabeta cell receptor and Repaglinide, Nateglinide, act to cause insulinsecretion by mechanism similar to Mitiglinide sulfonylureas Alpha-Inhibit carbohydrate digestion. Act at brush border of intestinalAcarbose, Miglitol, Glucosidase epithelium. Voglibose InhibitorsGlucagon- Diabetic patients may lack native GLP-1, and anlalogs act asExenatide, Exenatide LAR, Like substitutes. GLP-1 is an intestinalpeptide hormone that induces Liraglutide, ZP 10, Peptide(GLP)-glucose-dependent insulin secretion, controls gastric emptying, BN51077,1 Analogs inhibits appetite, and modulates secretion of glucagon andsomatostatin. Dipeptidyl Inhibit DPP-IV, a ubiquitous enzyme thatcleaves and inactivates LAF-237, p-32/98, MK- Peptidase GLP-1, thusinhibition of DPP-IV increases GLP-1 activity 431, P3298, NVP LAF 237,(DPP)-IV Inhibitors Pancreatic Inhibits lipases, thus inhibiting uptakeof dietary fat. This causes Orlistat Lipase weight loss, improvesinsulin sensitivity and lowers Inhibitors hyperglycemia. Amylin Act toaugment amylin, which acts with insulin by slowing Pramlintide Analogsglucose absorption from the gut and slows after-meal glucose releasefrom liver. Dopamine Thought to act to alleviate abnormal dailyvariations in central Bromocriptine D2 receptor neuroendocrine activitythat can contribute to metabolic and agonists immune system disordered.Immunosuppressants Suppress autoimmune response thought to be implicatedin Daclizumab, NBI 6024, Type I and possibly Type II diabetes. Example:Humanized TRX-TolerRx, OKT3- monoclonal antibody that recognizes andinhibits the alpha gamma-1-ala-ala subunit of IL-2 receptors; humanizedMab that binds to T cell CD3 receptor to block function of T-effectorcells that attack the body and cause autoimmune disease Insulin-likeRecombinant protein complex of insulin-like growth factor-1 andSomatomedin-1 binding growth binding protein-3; regulates the deliveryof somatomedin to target protein 3 factor-1 tissues. Reduces insulitisseverity and beta cell destruction agonists Insulin Insulin sensitizers,generally orally active S15261, Dexlipotam, CLX sensitizers 0901, R 483,TAK 654 Growth Mimic the action of native GHRF TH9507, SOM 230 hormonereleasing factor agonists Glucagon Inhibit glucagon action, stimulatinginsulin production and Liraglutide, NN 2501 antagonists secretion,resulting in lower postprandial glucose levels Diabetes type Preventsdestruction of pancreatic beta cells that occurs in type 1 Q-Vax, Damydvaccine 1 vaccine diabetes Sodium- Selectively inhibits the sodiumglucose co-transporter, which T 1095 glucose co- mediates renalreabsorption and intestinal absorption of glucose transporter tomaintain appropriate blood glucose levels. inhibitor Glycogen Inhibitglycogen phosphorylase, thus slowing release of glucose Ingliforibphosphorylase inhibitors Undefined Drugs that act in ways beneficial tothose with Type I or Type II FK 614, INGAP Peptide, R mechanismsDiabetes Mellitus, e.g., by reducing blood glucose and 1439 triglyceridelevels, whose mechanisms have not been elucidated. Antisense Bind to RNAand cause its destruction, thereby decreasing ISIS 113715oligonucleotides protein production from corresponding gene.Insulinotropin Stimulate insulin release CJC 1131 agonistsGluconeogenesis Inhibit gluconeogenesis, thus modulating blood glucoselevels CS 917 inhibitors Hydroxysteroid Inhibit hydroxysteroiddehydrogenase, which are responsible for BVT 3498 dehydrogenase excessglucocorticoid production and hence, visceral obesity inhibitors Beta 3Agonist for beta 3 adrenoceptor, decreases blood glucose and YM 178,Solabegron, adrenoceptor suppresses weight gain N5984, agonist Nitricoxide Decreases effects of NO NOX 700 antagonist Carnitine Inhibitscarnitine palmitoyltransferase ST 1326 palmitoyltransferase inhibitor

TABLE 14 Classes of Drugs for Treatment of Multiple Sclerosis ClassMechanism of Action Examples Recombinant IFN-beta has numerous effectson the immune system. Exact Interferon-beta-1b, interferons mechanism ofaction in MS not known Interferon-beta-1a Altered Ligands eithertemplated on sequence of myelin basic protein, or Glatiramer acetate,MBP- peptide containing randomly arranged amino acids (e.g., ala, lys,glu, tyr) 8298, Tiplimotide, AG-284 ligands whose structure resemblesmyelin basic protein, which is thought to be an antigen that plays arole in MS. Bind to the T-cell receptor but do not activate the T-cellbecause are not presented by an antigen-presenting cell.Chemotherapeutic Immunosuppressive. MS is thought to be an autoimmunedisease, Mitoxantrone, agents so chemotherapeutics that suppressimmunity improve MS Methotrexate, Cyclophosphamide ImmunosuppressantsAct via a variety of mechanisms to dampen immune response. Azathioprine,Teriflunomide, Oral Cladribine Corticosteroids Induce T-cell death andmay up-regulate expression of adhesion Methylprednisolone molecules inendothelial cells lining the walls of cerebral vessels, as well asdecreasing CNS inflammation. Monoclonal Bind to specific targets in theautoimmune cascade that produces Natalizumab, Daclizumab, Antibodies MS,e.g., bind to activated T-cells Altemtuzumab, BMS- 188667, E-6040,Rituximab, M1 MAbs, ABT 874, T- 0047 Chemokine Prevent chemokines frombinding to specific chemokine BX-471, MLN-3897, MLN- Receptor receptorsinvolved in the attraction of immune cells into the CNS 1202 Antagonistsof multiple sclerosis patients, and inhibiting immune cell migrationinto the CNS AMPA AMPA receptors bind glutamate, an excitatoryneurotransmitter, E-2007 Receptor which is released in excessivequantities in MS. AMPA Antagonists antagonists suppresses the damagecaused by the glutamate Recombinant GGF is associated with the promotionand survival of Recombinant Human GGF2 Human Glial oligodendrocytes,which myelinate neurons of the CNS. rhGGF Growth may help myelinateoligodendrocytes and protect the myelin Factor (GGF) sheath. T-cellMimic the part of the receptor in T cells that attack myelin NeuroVaxReceptor sheath, which activates regulatory T cells to decreasepathogenic Vaccine T-cells. Oral Various effects on the immune responsethat can modulate the Simvastatin, FTY-720, Oral Immunomodulatorsprocess of MS Glatiramer Acetate, FTY- 720, Pirfenidone, Laquinimod

In one embodiment, detection of miR-326 from a vesicle can be used tocharacterize multiple sclerosis, and one or more treatments selectedfrom Table 14 can be selected for the subject. In another embodiment,the theranosis can include selecting a therapy such as interferon β-1band interferon β-1a.

In another embodiment, a treatment can be selected for a subjectsuffering from Rheumatoid arthritis. One or more biomarkers, such as,but not limited to, 677CC/1298AA MTHFR, 677CT/1298AC MTHFR, 677CT MTHFR,G80AA RFC-1, 3435TT MDR1 (ABCB1), 3435TT ABCB1, AMPD1/ATIC/ITPA,IL1-RN3, HLA-DRB103, CRP, HLA-D4, HLA DRB-1, anti-citrulline epitopecontaining peptides, anti-A1/RA33, Erythrocyte sedimentation rate (ESR),C-reactive protein (CRP), SAA (serum amyloid-associated protein),rheumatoid factor, IL-1, TNF, IL-6, IL-8, IL-1Ra, Hyaluronic acid,Aggrecan, Glc-Gal-PYD, osteoprotegerin, RNAKL, carilage oligomericmatrix protein (COMP), and calprotectin, can be assessed from a vesiclefrom the subject. Based on one or more characteristics of the one ormore biomarkers, the subject can be determined to be a responder ornon-responder for a treatment, such as, but not limited to,Methotrexate, infliximab, adalimumab'etanercept, sulfasalazine, or acombination thereof.

Thus, a treatment can be selected for the subject suffering from anautoimmune condition, based on the biosignature of the subject's vesicle

Infectious Diseases

Assessing a vesicle can be used in the theranosis of an infectiousdisease such as a bacterial, viral or other infectious condition ordisease. An infectious or parasitic disease can arise from bacterial,viral, fungal, or other parasitic infection. For example, the disease orcondition may be Whipple's Disease, Prion Disease, cirrhosis,methicillin-resistant staphylococcus aureus, HIV, hepatitis, syphilis,meningitis, malaria, tuberculosis, or influenza.

An infectious or parasitic disease includes, but is not limited to,intestinal infectious diseases, tuberculosis, zoonotic bacterialdiseases, other bacterial diseases, human immunodeficiency virus hivinfection, poliomyelitis and other non arthropod borne viral diseases ofcentral nervous system, viral diseases accompanied by exanthem,arthropod borne viral diseases, other diseases due to viruses andchlamydiae, rickettsioses and other arthropod borne diseases, syphilisand other venereal diseases, other spirochetal diseases, mycoses,helminthiases, other infectious and parasitic diseases, and late effectsof infectious and parasitic diseases. Intestinal infectious diseasesinclude, but are not limited to cholera, typhoid and paratyphoid fevers,salmonella gastroenteritis, shigellosis, shigellosisunspecified,staphylococcal food poisoning, amoebiasis, acute amoebic dysenterywithout mention of abscess, chronic intestinal amoebiasis withoutmention of abscess, amoebic nondysenteric colitis, amoebic liverabscess, amoebic lung abscess, amoebic brain abscess, amoebic skinulceration, amoebic infection of other sites, unspecified amoebiasis,balantidiasis, giardiasis, coccidiosis, intestinal trichomoniasis,cryptosporidiosis, cyclosporiasisunspecifiedified protozoal intestinaldisease, intestinal infections due to other organisms, enteritis due torotavirus, enteritis due to other viral enteritis, intestinal infectiondue to other organism not elsewhere classified, ill defined intestinalinfections, colitis enteritis and gastroenteritis of presumed infectiousorigin.

A human immunodeficiency virus infection includes, but is not limited tohuman immunodeficiency virus infection with specified conditions, humanimmunodeficiency virus infection causing other specified, and otherhuman immunodeficiency virus infection.

A poliomyelitis and other non arthropod borne viral diseases of centralnervous system include, but are not limited to acute poliomyelitis, slowvirus infection of central nervous system, kuru, creutzfeld jakobdisease, meningitis due to enterovirus, other enterovirus diseases ofcentral nervous system, and other non arthropod borne viral diseases ofcentral nervous system. Viral diseases accompanied by exanthem include,but are not limited to smallpox, cowpox and paravaccinia, chickenpox,herpes zoster, herpes simplex, genital herpes, herpeticgingivostomatitis, herpetic disease, uncomplicated, measles, rubella,other viral exanthemata, fifth disease, unspecified viral exanthems,roseola infantum, other human herpesvirus encephalitis, other humanherpesvirus infections, other poxvirus infections, other orthopoxvirusinfections, monkeypox, other parapoxvirus infections, bovine stomatitis,sealpox, yatapoxvirus infections, tanapox, yaba monkey tumor virus,other poxvirus infections, and unspecified poxvirus infections.

Arthropod borne viral diseases include, but are not limited to yellowfever, dengue fever, mosquito borne viral encephalitis, encephalitis,mosquitounspecified, tick borne viral encephalitis, viral encephalitistransmitted by other and unspecified arthropods, arthropod bornehemorrhagic fever, ebolaunspecified, other arthropod borne viraldiseases, and unspecified west nile virus.

Other diseases due to viruses and chlamydiae include, but are notlimited to viral hepatitis, hepatitis a with hepatic coma, hepatitis awithout coma, hepatitis b with hepatic coma, hepatitis b without coma,acute, other specified viral hepatitis with mention of hepatic coma,other specified viral hepatitis without mention of hepatic coma,unspecified viral hepatitis c, viral hepatitis c without hepatic coma,viral hepatitis c with hepatic coma, hepatitis, viral, rabies, mumps,mumps, uncomplicated, ornithosis, specific diseases due to coxsackievirus, herpangina, hand, foot, mouth disease, mononucleosis, trachoma,other diseases of conjunctiva due to viruses and chlamydiae, otherdiseases due to viruses and chlamydiae, molluscum contagiosum, warts,all sites, condyloma acuminata, sweating fever, cat scratch disease,foot and mouth disease, cmv disease, viral infection in conditionsclassified elsewhere and of unspecified site, rhinovirus, hpv, andrespiratory syncytial virus. Rickettsioses and other arthropod bornediseases include, but are not limited to louse borne epidemic typhus,other typhus, tick borne rickettsioses, rocky mountain spotted fever,other rickettsioses, malaria, leishmaniasis, trypa omiasis, relapsingfever, other arthropod borne diseases, other specified arthropod bornediseases, lyme disease, and babesiosis.

A viral host includes, but is not limited to Adenovirus, Astrovirus,Avian influenza virus, Coxsackievirus, Dengue virus, Ebola virus,Echovirus, Enteric adenovirus, Enterovirus, Hantaviruses, Hepatitis Avirus, Hepatitis B virus, Hepatitis C virus, Hepatitis D virus,Hepatitis E virus, Herpes simplex virus (HSV), Human cytomegalovirus,Human immunodeficiency virus (HIV), Human papillomavirus (HPV),Influenza virus, Japanese encephalitis virus (JEV), Lassa virus, Marburgvirus, Measles virus, Mumps virus, Norovirus, Parainfluenza virus,Poliovirus, Rabies virus, Respiratory syncytial virus, Rotavirus,Rubella virus, SARS coronavirus, Tick-borne encephalitis virus (TBEV),Variola virus, West Nile virus, and Yellow fever virus. A fungal hostincludes, but is not limited to Candida albicans. A parasitic hostincludes, but is not limited to Plasmodium, Schistosoma mansoni, andTrichomonas vaginalis.

A bacterial host includes, but is not limited to Acinetobacterbaumannii, Bacillus anthracis, Bartonella, Bordetella pertussis,Borrelia, Brucella, Chlamydia pneumoniae, Chlamydia trachomatis,Clostridium botulinum, Corynebacterium diphtheriae, Coxiella burnetii,Ehrlichia, Enterococci, Enterovirulent Escherichia coli, Francisellatularensis, Haemophilus ducreyi, Helicobacter pylori, Klebsiellapneumoniae, Legionella pneumophila, Leptospira interrogans,Mycobacterium tuberculosis, Mycoplasma genitalium, Mycoplasmapneumoniae, Neisseria gonorrhoeae, Neisseria meningitidis, Orientiatsutsugamushi, Pseudomonas aeruginosa, Rickettsia, Salmonella, Shigella,Staphylococcus aureus, Streptococcus pneumoniae, Streptococcus pyogenes,Treponema pallidum, Ureaplasma urealyticum, Vibrio cholerae, Vibriovulnificus, and Yersinia pestis.

Zoonotic bacterial diseases includes, but is not limited to plague,bubonic plague, tularemia, anthrax, brucellosis, glanders, melioidosis,rat bite fever, listeriosis, erysipelothrix infection, andpasteurellosis. Other bacterial diseases include, but are not limited toleprosy, diseases due to other mycobacteria, diphtheria, whooping cough,streptococcal sore throat and scarlatina, strep throat, scarlet fever,erysipelas, meningococcal meningitis, tetanus, septicaemia, pneumococcalsepticemia, septicemia, gram negativeunspecified, septicemia, andactinomycotic infections.

Tuberculosis includes, but is not limited to primary tuberculousinfection, pulmonary tuberculosis, tuberculosis of meninges and centralnervous system, tuberculosis of intestines, peritoneum, and mesentericglands, tuberculosis of bones and joints, tuberculosis of vertebralcolumn, pott's disease, tuberculosis of genitourinary system,tuberculosis of other organs, erythema nodosum with hypersensitivityreaction in tuberculosis, bazin disease, tuberculosis of peripherallymph nodes, scrofula, and miliary tuberculosis.

Syphilis and other venereal diseases include, but are not limited tocongenital syphilis, early syphilis, symptomatic, syphilis, primary,genital, early syphilis, latent, cardiovascular syphilis, neurosyphilis,other forms of late syphilis, with symptoms, late syphilis, latent,other and unspecified syphilis, gonococcal infections, gonorrhoea,acute, lower gu tract, gonococcal conjunctivitis, and nongonococcalurethritis. Other spirochetal diseases include, but are not limited toleptospirosis, Vincent's angina, yaws, and pinta. Mycoses include, butare not limited to dermatophytosis, dermatophytosis of scalp/beard,onychomycosis, dermatophytosis of hand, tinea cruris, tinea pedis, tineacorporis, dermatomycosis, other and unspecified, tinea versicolor,dermatomycosisunspecified, candidiasis, moniliasis, oral, moniliasis,vulva/vagina, monilial balanitis, moniliasis, skin/nails,coccidioidomycosis, histoplasmosis, histoplasma infectionunspecified,blastomycotic infection, other mycoses, and opportunistic mycoses.

Helminthiases include, but are not limited to schistosomiasisbilharziasis, other trematode infections, echinococcosis, other cestodeinfection, trichi is, filarial infection and dracontiasis,ancylostomiasis and necatoriasis, other intestinal helminthiases,ascariasis, anisakiasis, strongyloidiasis, trichuriasis, enterobiasis,capillariasis, trichostrongyliasis, other and unspecified helminthiases,and unspecified intestinal parasitism. Other infectious and parasiticdiseases include, but are not limited to toxoplasmosis,toxoplasmosisunspecified, trichomoniasis, urogenital trichomoniasis,trichomonal vaginitis, trichomoniasis, urethritis, pediculosis andphthirus infestation, pediculosis, head lice, pediculosis, body lice,pediculosis, pubic lice, pediculosisunspecified, acariasis, scabies,chiggers, sarcoidosis, ainhum, behcet's syndrome, pneumocystosis,psorospermiasis, and sarcosporidiosis. Late effects of infectious andparasitic diseases include, but are not limited to late effects oftuberculosis, and late effects of polio.

Infectious Disease: Biosignature

A biosignature of a vesicle can be assessed to provide a theranosis fora subject. The biosignature of the vesicle can comprise one or morebiomarkers such as, but not limited to, any one or more biomarkers asdescribed herein, such as, but not limited to, those listed in FIG. 1for infection diseases, and FIGS. 24 and 43.

In some embodiments, an infectious disease can be characterized bydetecting a component of a pathogen, such as a virus, bacteria, or otherinfectious agent, in a vesicle. For example, the component can be ABCtransporters (Candida albicans), ABC transporters (Enterococci), AMA-1(Apical membrane antigen 1), ATPase, Aac(6′)-Aph(2″) enzyme, Ace(Accessory cholera enterotoxin), Acf (Accessory colonization factor),Acr (α-crystallin) protein, AhpC and AhpD, Amyloid-β, AroC, Attachmentglycoprotein (G) (Respiratory syncytial virus), Autolysin(N-acetylmuramoyl-L-alanine amidase), BacA, BmpA (P39), Botulinumneurotoxins, BvgA, -S, and -R, BvrR-BvrS, C4BP(C4b-binding protein), C5apeptidase, CAMP factor (cohemolysin), CBP (Choline binding protein), CMEtype β-lactamase, CSP (Circumsporozoite protein), CT (cholera toxin),CTX-M metallo-β-lactamase, CagA (cytotoxin-associated antigen), Capsidprotein (C) (Dengue virus), Capsid protein (C) (Japanese encephalitisvirus), Capsid protein (C) (Tick-borne encephalitis virus), Capsidprotein (C) (West Nile virus), Capsid protein (C) (Yellow fever virus),Capsid protein (Astrovirus), Capsid protein (Coxsackievirus), Capsidprotein (Echovirus), Capsid protein (Enterovirus), Capsid protein(Hepatitis A virus), Capsid protein (Poliovirus), Capsid protein(Rotavirus), Catechol siderophore ABC transporter, Com-1, CrmB (Cytokineresponse modifier), Cytolysin, D-Ala-D-Lac ligase, DHFR (Dihydrofolatereductase), DHPS (Dihydropteroate synthetase), DbpA (Decorin-bindingprotein A), Diphtheria toxin, Dot/Icm complex, E1 and E2 proteins(Rubella virus), E1A protein (Adenovirus), E1A protein (Entericadenovirus), E1B protein (Adenovirus), E1B protein (Enteric adenovirus),E2 early transcription region 2, E3 protein (Adenovirus), E4 protein(Adenovirus), E6 early transcription region 6, E7 early transcriptionregion 7, EF (Edema factor), ESAT-6 and CFP-10, Elastase (Vibriovulnificus), Env, Envelope glycoprotein (E) (Dengue virus), Envelopeglycoprotein (E) (Japanese encephalitis virus), Envelope glycoprotein(E) (Tick-borne encephalitis virus), Envelope glycoprotein (E) (WestNile virus), Envelope glycoprotein (E) (Yellow fever virus), Esp(Enterococcal surface protein), Esp (Type III System-Secreted Proteins),F1 capsule (F1 antigen), FH (Factor H), FHA (Filamentous hemagglutinin),Falcipain 1/2, Fiber protein (Adenovirus), Fiber protein (Entericadenovirus), Fibronectin binding protein II (Protein F/sfbII)(Streptococcus pyogenes), Fibronectin binding protein (Leptospirainterrogans), Fibronetin binding protein (FBP54) (Streptococcuspyogenes), Fimbrial protein, Flagellin (FlaB and -A) (H. pylori),Flagellin (H-antigen) (Escherichia coli), Flagellin (H-antigen)(Salmonella), Flagellin (Vibrio vulnificus), FopA (43 kDa lipoprotein),Fusion protein (F) (Mumps virus), Fusion protein (F) (Parainfluenzavirus), Fusion protein (F) (Respiratory syncytial virus), G6PD(Glucose-6-phosphate dehydrogenase), GES (Guiana extended-spectrumβ-lactamase), GTP cyclohydrolase, Gag, Glycoprotein (G) (Rabies virus),Glycoprotein (GP) (Ebola virus), Glycoprotein (GP) (Lassa virus),Glycoprotein (GP) (Marburg virus), Glycoproteins (Gn/Gc) (Hantaviruses),HMW (Cytadherence accessory protein), HRP2 (Histidine-rich protein 2),Hemagglutinin (Avian influenza virus), Hemagglutinin (Influenza virus),Hemagglutinin (Measles virus), Hemagglutinin (Variola virus),Hemagglutinin-esterase glycoprotein (HE), Hemagglutinin-neuraminidase(HN) (Mumps virus), Hemagglutinin-neuramimidate (HN) (Paraninfluenzavirus), Hemolysin (Vvh), Hexon protein (Adenovirus), Hexon protein(Enteric adenovirus), Hsp60 (Heat shock protein 60), Hyaluronate lyase,Hyaluronidase, IMP metallo-β-lactamase (Acinetobacter baumannii), IMPmetallo-β-lactamase (Klebsiella pneumoniae), IcsA and IcsB, IgA protease(Neisseria gonorrhoeae), IgA1 protease (Streptococcus pneumoniae), IgGand IgM for HSV 1/2, InhA, Intimin, InvA (Rickettsia), Invasin(Escherichia coli), Invasin (Yersinia pestis), IpaA, -B, -C, -D and -H,KPC metallo-β-lactamase, KatG, L protein (Lassa virus), L1 latetranscription region 1, LF (Lethal factor), LSA1 (Liver-stage antigen1), LT (heat labile toxin), LcrV (V antigen), LigA and LigB,Lipoprotein, M protein, MSP (Merozoite surface protein), Matrix protein(M) (Rabies virus), Matrix protein (M) (Respiratory syncytial virus),Matrix protein (Avian influenza virus), Matrix protein (Influenzavirus), MexAB-OprM, MexCD-OprJ, MexEF-OprN, MexXY-OprM, Mip (Macrophageinfectivity potentiator), NSE (Neuron-specific enolase), Nef,Neuraminidase (Avian influenza virus), Neuraminidase (Influenza virus),Neuraminidase (Streptococcus pneumoniae), Non-structural protein (NS)(Respiratory syncytial virus), Non-structural protein 1 (NS1) (Denguevirus), Non-structural protein 1 (NS1) (Japanese encephalitis virus),Non-structural protein 1 (NS1) (Tick-borne encephalitis virus),Non-structural protein 1 (NS1) (West Nile virus), Non-structural protein1 (NS1) (Yellow fever virus), Non-structural protein 2A (NS2A) (Denguevirus), Non-structural protein 2A (NS2A) (Japanese encephalitis virus),Non-structural protein 2A (NS2A) (Tick-borne encephalitis virus),Non-structural protein 2A (NS2A) (West Nile virus), Non-structuralprotein 2A (NS2A) (Yellow fever virus), Non-structural protein 2B (NS2B)(Dengue virus), Non-structural protein 2B (NS2B) (Japanese encephalitisvirus), Non-structural protein 2B (NS2B) (Tick-borne encephalitisvirus), Non-structural protein 2B (NS2B) (West Nile virus),Non-structural protein 2B (NS2B) (Yellow fever virus), Non-structuralprotein 3 (NS3) (Dengue virus), Non-structural protein 3 (NS3) (Japaneseencephalitis virus), Non-structural protein 3 (NS3) (Tick-borneencephalitis virus), Non-structural protein 3 (NS3) (West Nile virus),Non-structural protein 3 (NS3) (Yellow fever virus), Non-structuralprotein 4 (Rotavirus), Non-structural protein 4A (NS4A) (Dengue virus),Non-structural protein 4A (NS4A) (Japanese encephalitis virus),Non-structural protein 4A (NS4A) (Tick-borne encephalitis virus),Non-structural protein 4A (NS4A) (West Nile virus), Non-structuralprotein 4A (NS4A) (Yellow fever virus), Non-structural protein 4B (NS4B)(Dengue virus), Non-structural protein 4B (NS4B) (Japanese encephalitisvirus), Non-structural protein 4B (NS4B) (Tick-borne encephalitisvirus), Non-structural protein 4B (NS4B) (West Nile virus),Non-structural protein 4B (NS4B) (Yellow fever virus), Non-structuralprotein 5 (NS5) (Dengue virus), Non-structural protein 5 (NS5) (Japaneseencephalitis virus), Non-structural protein 5 (NS5) (Tick-borneencephalitis virus), Non-structural protein 5 (NS5) (West Nile virus),Non-structural protein 5 (NS5) (Yellow fever virus), Non-structuralproteins (Avian influenza virus), Non-structural proteins (Influenzavirus), Nucleocapsid (Hantaviruses), Nucleocapsid (Measles virus),Nucleocapsid (Parainfluenza virus), Nucleocapsid (SARS coronavirus),Nucleoprotein (N) (Rabies virus), Nucleoprotein (NP) (Respiratorysyncytial virus), Nucleoprotein (major nucleoprotein) (Marburg virus),Nucleoprotein (Avian influenza virus), Nucleoprotein (Ebola virus),Nucleoprotein (Influenza virus), Nucleoprotein (Lassa virus), ORF 1(Hepatitis E virus), ORF2 (Hepatitis E virus), ORF3 (Hepatitis E virus),OXA metallo-β-lactamase (Acinetobacter baumannii), OXAmetallo-β-lactamase (Klebsiella pneumoniae), OmpA and OmpB (Rickettsia),OmpL1 (Leptospira interrogans), OmpQ (Outer membrane porin protein)(Bordetella pertussis), OmpS (Legionella pneumophila), Opacity factor,OprD, Osp (Outer surface protein), Outer membrane proteins (Chlamydiapneumoniae), Outer membrane proteins (Ehrlichia), P1 adhesin, P30adhesin, PA (Protective antigen), PBP (Penicillin-binding protein),PCRMP 1-4 (Cysteine repeat modular proteins), PER metallo-β-lactamase,Pat1, Peptidoglycan (murein) hydrolase, Pertactin (p69), Pertussistoxin, PfEMP1 (Plasmodium falciparum erythrocyte membrane protein-1),Phosphoprotein (P) (Respiratory syncytial virus), Phosphoprotein(Measles virus), Pla (plasminogen activator), Plasminogen-bindingprotein, Pld, Pneumolysin, Pol, Poly-D-glutamic acid capsule, Polymerase(L) (Rabies virus), Porin, Premembrane/membrane protein (PrM/M) (Denguevirus), Premembrane/membrane protein (PrM/M) (Japanese encephalitisvirus), Premembrane/membrane protein (PrM/M) (Tick-borne encephalitisvirus), Premembrane/membrane protein (PrM/M) (West Nile virus),Premembrane/membrane protein (PrM/M) (Yellow fever virus), Proteins fortwo-component regulatory systems (Ehrlichia), Proteins for two-componentregulatory systems (Mycobacterium tuberculosis), Proteins of gB, gC, gD,gH, and gL, PsaA, PspA (Pneumococcal surface protein A), PurE, Pyrogenicexotoxins, RBP 1/2 (Reticulocyte binding protein 1/2), RdRp (RNAdependant RNA polymerase) (Norovirus), RdRp (RNA dependent RNApolymerase) (Astrovirus), RdRp (RNA dependent RNA polymerase) (SARScoronavirus), Rev, RfbE, RibD and RibE, Rmp, S-layer protein, S100B(S100 protein β chain), SHV metallo-β-lactamase, SIMmetallo-β-lactamase, ST (heat stable toxin), Salmonella plasmidvirulence (SPV) proteins, Serine protease (Astrovirus), ShET1/2, Shigatoxin (Verotoxin), SipA (Salmonella Invasion Protein A), SlyA, Smallhydrophobic protein, Sop (Salmonella outer protein), Spike glycoprotein(S), Streptococcal DNase, Streptogramin A acetyltransferase,Streptokinase, Streptolysin O, StxA/B (Shiga toxin A/B), SucB(Dihydrolipoamide succinyltransferase) (Mycobacterium tuberculosis),SucB (dihydrolipoamide succinyltransferase) (Coxiella burnetii), Syc(Yop chaperones), T protein, TCP (toxin-coregulated pilus), TEMmetallo-β-lactamase, TRAP (Thrombospondin-related anonymous protein),Tat, Tau-protein, TcfA (Tracheal colonization factor), Tir (Translocatedintimin receptor), TlyA and TlyC, ToxR (toxin regulatory protein), Tul4(17 kDa lipoprotein), Type IV pili, Urease (Brucella), Urease(Helicobacter pylori), VEB metallo-β-lactamase, VETF (Virus earlytranscription factor), VIM metallo-β-lactamase (Acinetobacterbaumannii), VIM metallo-β-lactamase (Klebsiella pneumoniae), VP1(Norovirus), VP2 (Norovirus), VP24 (Ebola virus), VP24 (Marburg virus),VP30 (minor nucleoprotein) (Ebola virus), VP30 (minor nucleoprotein)(Marburg virus), VP35 (P-like protein) (Ebola virus), VP35 (P-likeprotein) (Marburg virus), VP40 (Matrix Protein) (Ebola virus), VP40(Matrix Protein) (Maburg virus), VacA (vacuolating cytotoxin), Vag8(virulence-activated gene 8), Vif, VirB type IV secretion system, VlsE(35 kDa lipoprotein), Vpr, Vpu/Vpx, XerD, Yops (Yersinia outermembraneproteins), Ysc (Yop secretion apparatus), Z protein (Lassa virus), Zot(zonula occuldens toxin), gG1 (HSV-1) and gG2 (HSV-2), p41i, p83, andp100, pLDH (Plasmodium lactate dehydrogenase), α/β/γ proteins, 120 kDagene, 16S and 5S rRNA genes (Legionella pneumophila), 16S rRNA(Bartonella), 16S rRNA (Borrelia), 16S rRNA (Brucella), 16S rRNA(Ehrlichia), 16S rRNA (Klebsiella pneumoniae), 16S rRNA (Orientiatsutsugamushi), 16S rRNA (Rickettsia), 16S rRNA gene (Acinetobacterbaumannii), 16S rRNA gene (Chlamydia pneumoniae), 16S rRNA gene(Clostridium botulinum), 16S rRNA gene (Mycoplasma pneumoniae), 16S rRNAgene (Neisseria gonorrhoeae), 16S rRNA gene (Vibrio vulnificus), 16S-23SrRNA intergenic spacer (Bartonella), 16S-23S rRNA intergenic spacer(Coxiella burnetii), 17 kDa gene, 18S ssrRNA, 23S rRNA gene(Acinetobacter baumannii), 23S rRNA gene (Neisseria gonorrhoeae), 2Cgene, 3′ NCR (Dengue virus), 3′ NCR (Japanese encephalitis virus), 3′NCR (Tick-borne encephalitis virus), 3′ NCR (West Nile virus), 3′ NCR(Yellow fever virus), 5′ NCR (Coxsackievirus), 5′ NCR (Dengue virus), 5′NCR (Echovirus), 5′ NCR (Enterovirus), 5′ NCR (Japanese encephalitisvirus), 5′ NCR (Polioviurs), 5′ NCR (Tick-borne encephalitis virus), 5′NCR (West Nile virus), 5′ NCR (Yellow fever virus), 56 kDa gene, A13Lgene, ARE1 gene, ATF2 gene, B12R gene, B6R gene, B8R gene, C gene(Dengue virus), C gene (Japanese encephalitis virus), C gene (Tick-borneencephalitis virus), C gene (West Nile virus), C gene (Yellow fevervirus), C3L gene, CDR 1/2 genes, E gene (Dengue virus), E gene (Japaneseencephalitis virus), E gene (Tick-borne encephalitis virus), E gene(West Nile virus), E gene (Yellow fever virus), E1 and E2 genes, E1Agene (Adenovirus), E1A gene (Enteric adenovirus), E1B gene (Adenovirus),E1B gene (Enteric adenovirus), E2 gene, E3 gene (Adenovirus), E3L gene,E4 gene (Adenovirus), E6 gene, E7 gene, ERG genes, ESAT-6 and CFP-10genes, F gene (Mumps virus), F gene (Parainfluenza virus), F gene(Respiratory syncytial virus), G gene (Rabies virus), G gene(Respiratory syncytial virus), GP gene (Ebola virus), GP gene (Lassavirus), GP gene (Marburg virus), H gene (Measles virus), HA gene (Avianinfluenza virus), HA gene (Influenza virus), HE gene (SARS Coronavirus),HN gene (Mumps virus), HN gene (Parainfluenza virus), IS100, IS1081,IS1533 (Leptospira interrogans), IS285, IS481 (BP0023), IS6110, IS711(Brucella), ISFtu, J7R gene, L gene (Lassa virus), L gene (Rabiesvirus), L segment, L1 gene, LEE (locus of enterocyte effacement), Longcontrol region (LCR), M gene (Rabies virus), M gene (Respiratorysyncytial virus), M genes (Avian influenza virus), M genes (Influenzavirus), M segment, MDR1 gene, MEC3 gene, N gene (Measles virus), N gene(Rabies virus), N gene (SARS coronavirus), NA gene (Avian influenzavirus), NA gene (Influenza virus), NC gene (Parainfluenza virus), NPgene (Avian influenza virus), NP gene (Ebola virus), NP gene (Influenzavirus), NP gene (Lassa virus), NP gene (Marburg virus), NP gene(Respiratory syncytial virus), NS gene (Avian influenza virus), NS gene(Influenza virus), NS gene (Respiratory syncytial virus), NS1 gene(Dengue virus), NS1 gene (Japanese encephalitis virus), NS1 gene(Tick-borne encephalitis virus), NS1 gene (West Nile virus), NS1 gene(Yellow fever virus), NS2A gene (Dengue virus), NS2A gene (Japaneseencephalitis virus), NS2A gene (Tick-borne encephalitis virus), NS2Agene (West Nile virus), NS2A gene (Yellow fever virus), NS2B gene(Dengue virus), NS2B gene (Japanese encephalitis virus), NS2B gene(Tick-borne encephalitis virus), NS2B gene (West Nile virus), NS2B gene(Yellow fever virus), NS3 gene (Dengue virus), NS3 gene (Japaneseencephalitis virus), NS3 gene (Tick-borne encephalitis virus), NS3 gene(West Nile virus), NS3 gene (Yellow fever virus), NS4 gene (Rotavirus),NS4A gene (Dengue virus), NS4A gene (Japanese encephalitis virus), NS4Agene (Tick-borne encephalitis virus), NS4A gene (West Nile virus), NS4Agene (Yellow fever virus), NS4B gene (Dengue virus), NS4B gene (Japaneseencephalitis virus), NS4B gene (Tick-borne encephalitis virus), NS4Bgene (West Nile virus), NS4B gene (Yellow fever virus), NS5 gene (Denguevirus), NS5 gene (Japanese encephalitis virus), NS5 gene (Tick-borneencephalitis virus), NS5 gene (West Nile virus), NS5 gene (Yellow fevervirus), ORF 1a (Astrovirus), ORF1b (Astrovirus), ORF 2 (Astrovirus),ORF1 (Hepatitis E virus), ORF1 (Norovirus), ORF2 (Hepatitis E virus),ORF2 (Norovirus), ORF3 (Hepatitis E virus), ORF3 (Norovirus), P gene(Measles virus), P gene (Respiratory syncytial virus), PDH1 gene,Peptidyltransferase mutations, Plasmids (QpH1, QpRS, QpDG, QpDV), PrM/Mgene (Dengue virus), PrM/M gene (Japanese encephalitis virus), PrM/Mgene (Tick-borne encephalitis virus), PrM/M gene (West Nile virus),PrM/M gene (Yellow fever virus), RdRp gene in ORF 1ab (SARScoronavirus), S gene (SARS coronavirus), S segment, SH gene (Mumpsvirus), SNP (single nucleotide polymorphism), Salmonella pathogenicityisland (SPI), Salmonella plasmid virulence (SPV) operon, ShET1/2 genes,VNTR (variable number tandem repeat) (Bacillus anthracis), VNTR(variable number tandem repeat) (Brucella), VNTR (variable number tandemrepeat) (Francisella tularensis), VNTR (variable number tandem repeat)(Yersinia pestis), VP24 gene (Ebola virus), VP24 gene (Marburg virus),VP30 gene (Ebola virus), VP30 gene (Marburg virus), VP35 gene (Ebolavirus), VP35 gene (Marburg virus), VP40 gene (Ebola virus), VP40 gene(Marburg virus), Z gene (Lassa virus), aac(3) gene, aac(6′) gene,aac(6′)-aph(2″) gene, aad gene, ace gene, acpA gene, agrBDCA locus, ahpCand ahpD genes, arlRS locus, atxA gene, bclA gene, blaCTX-M gene, blaGESgene, blaGIM gene (Pseudomonas aeruginosa), blaIMP gene (Acinetobacterbaumannii), blaIMP gene (Klebsiella pneumoniae), blaIMP gene(Pseudomonas aeruginosa), blaKPC gene, blaOXA gene (Acinetobacterbaumannii), blaOXA gene (Klebsiella pneumoniae), blaOXA gene(Pseudomonas aeruginosa), blaSHV gene, blaSIM gene (Klebsiellapneumoniae), blaSIM gene (Pseudomonas aeruginosa), blaTEM gene, blaVIMgene (Acinetobacter baumannii), blaVIM gene (Klebsiella pneumoniae),blaVIM gene (Pseudomonas aeruginosa), bvg locus (bvgA, -S, and -Rgenes), cagA gene, cap locus (capB, -C, and -A genes) (Bacillusanthracis), cap operon (capB and -C) (Francisella tularensis), capsidgene (Coxsackievirus), capsid gene (Echovirus), capsid gene(Enterovirus), capsid gene (Hepatitis A virus), capsid gene(Poliovirus), capsid gene (Rotavirus), cme gene, cnt genes, com-1 gene,cppB gene, cps gene, crmB gene, ctx gene, cya gene, cyl gene, eaeA gene,east gene (Escherichia coli), env gene, ery gene, esp gene(Enterococci), esp genes (Escherichia coli), fiber gene (Adenovirus),fiber gene (Enteric adenovirus), fimbriae genes, flaB gene (Borrelia),flaB gene (Leptospira interrogans), flagellin genes, fljA, fljB, andfliC genes, fopA gene, ftsZ gene, gG1 and gG2 gens, gag gene, genes fortwo-component regulatory systems, genes of gB, gC, gD, gH, and gL, gerXlocus (gerXC, -A, and -B genes), glpQ gene, gltA (citrate synthase) gene(Bartonella), gltA (citrate synthase) gene (Rickettsia), groEL gene(Bartonella), groEL gene (Orientia tsutsugamushi), groESL gene(Chlamydia pneumoniae), gyrA and gyrB genes (Pseudomonas aeruginosa),gyrA gene (Neisseria gonorrhoeae), gyrB gene (Bacillus anthracis), hexongene (Adenovirus), hexon gene (Enteric adenovirus), hin gene, hlyA gene,hmw genes, hspX (Rv2031c) gene, htpAB associated repetitive element(IS1111a), hyl gene, icsA and icsB genes, ileS gene, inhA gene, inv gene(Escherichia coli), inv gene (Salmonella), ipaA, -B, -C, -D and -Hgenes, katG gene, lef gene, letA gene, lidA gene, lpsB gene, lrgABlocus, luxS gene, lytA gene, lytRS locus, mecA gene, mglA gene, mgrA(rat) gene, mip gene, mtgA gene, mucZ gene, multigene families, mupAgene, nanA and nanB genes, nef gene, omp genes (Brucella), omp genes(Chlamydia pneumonia), ompA and B gene (Rickettsia), ompQ gene, opagenes, osp genes, p1 gene, p30 gene, pagA gene, pap31 gene, parC andparE genes (Pseudomonas aeruginosa), parC gene (Neisseria gonorrhoeae),per gene, pilQ gene, ply gene, pmm gene, pol gene, porA and porB genes,prn4 (pertactin) gene, psaA gene, pspA gene, pst1 fragment and HL-1/HR-1primers, ptx (promoter region and complete gene), rap 1/2 genes, revgene, rpo18 gene, rpoB gene, rpoS gene, rpsL gene, rrf(5S)-rrl(23S)intergenic spacer, rsk gene, rtx gene (Vibrio vulnificus), rtxA gene(Legionella pneumophila), sap gene (Bacillus anthracis), sar gene, satA(vatD) and satG (vatE) genes, sca4 gene, secY gene, stx (vt) gene,stxA/B (stx1/2) gene, sucB gene, tat gene, tcp gene, tir gene, tox gene,toxR gene, tul4 gene, urease genes, vacA gene, van A-E genes, veb gene,vif gene, viuB gene, vpr gene, vpu/vpx gene, vvh (Vibrio vulnificushemolysin) gene, vvpE (Vibrio vulnificus elastase) gene, wboA gene, wzy(O-antigen polymerase) gene, zot gene, α/β/γ genes, C-polysaccharide(rhamnose/N-acetylglucosamine), CPS (capsular polysaccharide), Cyclicβ-1,2 glucan, Hyaluronic acid capsule, LPS (lipopolysaccharide)(Bartonella), LPS (lipopolysaccharide) (Brucella), LPS(lipopolysaccharide) (Coxiella burnetii), LPS (lipopolysaccharide)(Rickettsia), LPS (lipopolysaccharide) (Vibrio vulnificus), O-antigen(Escherichia coli), O-antigen (Salmonella), O-antigen (Vibrio cholerae),Vi-antigen (Salmonella), or Catechol siderophore.

Infectious Disease: Standards of Care

Determining the biosignature of a vesicle, the amount of vesicles, orboth, of a sample from a subject suffering from an infectious orparasitic disease, disorder or disease can be used to select a standardof care for the subject. An infectious or parasitic disease can betreated according to symptoms associated with the condition. Thestandard of care includes, for example, treating with one or moreantibiotics and antiviral agents.

An antibiotic includes, but not limited to, Amikacin, Gentamicin,Kanamycin, Neomycin, Netilmicin, Streptomycin, Tobramycin, Paromomycin,Geldanamycin, Herbimycin, Loracarbef, Ertapenem, Doripenem,Imipenem/Cilastatin, Meropenem, Cefadroxil, Cefazolin, Cefalotin orCefalothin, Cefalexin, Cefaclor, Cefamandole, Cefoxitin, Cefprozil,Cefuroxime, Cefixime, Cefdinir, Cefditoren, Cefoperazone, Cefotaxime,Cefpodoxime, Ceftazidime, Ceftibuten, Ceftizoxime, Ceftriaxone,Cefepime, Ceftobiprole, Teicoplanin, Vancomycin, Azithromycin,Clarithromycin, Dirithromycin, Erythromycin, Roxithromycin,Troleandomycin, Telithromycin, Spectinomycin, Aztreonam, Amoxicillin,Ampicillin, Azlocillin, Carbenicillin, Cloxacillin, Dicloxacillin,Flucloxacillin, Mezlocillin, Meticillin, Nafcillin, Oxacillin,Penicillin, Piperacillin, Ticarcillin, Bacitracin, Colistin, PolymyxinB, Ciprofloxacin, Enoxacin, Gatifloxacin, Levofloxacin, Lomefloxacin,Moxifloxacin, Norfloxacin, Ofloxacin, Trovafloxacin, Grepafloxacin,Sparfloxacin, Temafloxacin, Mafenide, Sulfonamidochrysoidine,Sulfacetamide, Sulfanilimide, Sulfasalazine, Sulfisoxazole,Trimethoprim, Trimethoprim-, Sulfamethoxazole, Demeclocycline,Doxycycline, Minocycline, Oxytetracycline, Tetracycline, Sulfadiazine,Sulfamethizole, Arsphenamine, Chloramphenicol, Clindamycin, Lincomycin,Ethambutol, Fosfomycin, Fusidic acid, Furazolidone, Isoniazid,Linezolid, Metronidazole, Mupirocin, Nitrofurantoin, Platensimycin,Pyrazinamide, Quinupristin or Dalfopristin, Rifampicin, Thiamphenicol,Timidazole, Dapsone, and Clofazimine. Examples of antibiotics are alsolisted in Table 15.

An antiviral agent includes, but is not limited to Abacavir, Aciclovir,Acyclovir, Adefovir, Amantadine, Amprenavir, Ampligen, Arbidol,Atazanavir, Atripla, Boceprevir, Cidofovir, Combivir, Darunavir,Delavirdine, Didanosine, Docosanol, Edoxudine, Efavirenz, Emtricitabine,Enfuvirtide, Entecavir, Famciclovir, Fomivirsen, Fosamprenavir,Foscarnet, Fosfonet, Ganciclovir, Ibacitabine, Immunovir, Idoxuridine,Imiquimod, Indinavir, Inosine, Interferon type III, Interferon type II,Interferon type I, Lamivudine, Lopinavir, Loviride, Maraviroc,Moroxydine, Nelfinavir, Nevirapine, Nexavir, Oseltamivir, Peginterferonalfa-2a, Penciclovir, Peramivir, Pleconaril, Podophyllotoxin,Raltegravir, Ribavirin, Rimantadine, Ritonavir, Pyramidine, Saquinavir,Stavudine, Tea tree oil, Tenofovir, Tenofovir disoproxil, Tipranavir,Trifluridine, Trizivir, Tromantadine, Truvada, Valaciclovir,Valganciclovir, Vicriviroc, Vidarabine, Viramidine, Zalcitabine,Zanamivir, and Zidovudine.

TABLE 15 Examples of Antibiotic Drugs and their Structure ClassStructure Class Examples of Antibiotics within Structure Class AminoAcid Derivatives Azaserine, Bestatin, Cycloserine,6-diazo-5-oxo-L-norleucine Aminoglycosides Armastatin, Amikacin,Gentamicin, Hygromicin, Kanamycin, Streptomycin BenzochinoidesHerbimycin Carbapenems Imipenem, Meropenem Coumarin-glycosidesNovobiocin Fatty Acid Derivatives Cerulenin Glucosamines1-deoxynojirimycin Glycopeptides Bleomycin, Vancomycin ImidazolesMetroidazole Penicillins Benzylpenicillin, Benzathine penicillin,Amoxycillin, Piperacillin Macrolides Amphotericin B, Azithromycin,Erythromycin Nucleosides Cordycepin, Formycin A, Tubercidin PeptidesCyclosporin A, Echinomycin, Gramicidin Peptidyl NucleosidesBlasticidine, Nikkomycin Phenicoles Chloramphenicol, ThiamphenicolPolyethers Lasalocid A, Salinomycin Quinolones 8-quinolinol, Cinoxacin,Ofloxacin Steroids Fusidic Acid Sulphonamides Sulfamethazine,Sulfadiazine, Trimethoprim Tetracyclins Oxytetracyclin, Minocycline,Duramycin

In one embodiment, a subject has an HIV infection. One or morebiomarkers, such as, but not limited to p24 antigen, TNF-alpha, TNFR-11,CD3, CD14, CD25, CD27, Fas, FasL, beta2 microglobulin, neopterin, HIVRNA, and HLA-B *5701, can be assessed from a vesicle from the subject.Based on one or more characteristics of the one or more biomarkers, thesubject can be determined to be a responder or non-responder for atreatment, such as, but not limited to, Zidovudine, Didanosine,Zalcitabine, Stavudine, Lamivudine, Saquinavir, Ritonavir, Indinavir,Nevirane, Nelfinavir, Delavirdine, Stavudine, Efavirenz, Etravirine,Enfuvirtide, Darunavir, Abacavir, Amprenavir, Lonavir/Ritonavirc,Tenofovir, Tipranavir, or a combination thereof.

Thus, a treatment can be selected for the subject suffering from aninfectious disease or condition, based on the biosignature of thesubject's vesicle.

Neurology

Assessing a vesicle can be used in the theranosis of a neurologicaldisease, such as Multiple Sclerosis (MS), Parkinson's Disease (PD),Alzheimer's Disease (AD) (non-inflammatory and inflammatory),schizophrenia, bipolar disorder, depression, autism, Prion Disease,Pick's disease, dementia, Huntington disease (HD), Down's syndrome,cerebrovascular disease, Rasmussen's encephalitis, viral meningitis,neurospsychiatric systemic lupus erythematosus (NPSLE), amyotrophiclateral sclerosis, Creutzfeldt-Jacob disease,Gerstmann-Straussler-Scheinker disease, transmissible spongiformencephalopathy, ischemic reperfusion damage (e.g. stroke), brain trauma,microbial infection, or chronic fatigue syndrome.

A neurological disorder includes, but is not limited to inflammatorydiseases of the central nervous system, hereditary and degenerativediseases of the central nervous system, pain, other headache syndromes,other disorders of the central nervous system, and disorders of theperipheral nervous system. Inflammatory diseases of the central nervoussystem include, but are not limited to bacterial meningitis, meningitis,hemophilus, meningitis, bacterial, meningitis due to other organisms,cryptococcal meningitis, meningitis of unspecified cause, encephalitis,myelitis, and encephalomyelitis, postinfectious encephalitis,unspecified encephalitis, intracranial and intraspinal abscess,phlebitis and thrombophlebitis of intracranial venous sinuses, venoussinus thrombosis, intracranial, late effects of intracranial abscess orpyogenic infection, sleep disorders, unspecified organic insomnia,insomnia due to medical condition classified elsewhere, and insomnia dueto mental disorder. Hereditary and degenerative diseases of the centralnervous system include, but are not limited to cerebral degenerationsusually manifest in childhood, leukodystrophy, krabbe disease, pelizaeusmerzbacher disease, cerebral lipidoses, tay sacks disease, othercerebral degenerations, alzheimer's, pick's disease, senile degenerationof brain, communicating hydrocephalus, obstructive hydrocephalus,idiopathic normal pressure hydrocephalus, other cerebral degeneration,reye's syndrome, dementia with lewy bodies, mild cognitive impairment,so stated, Parkinson's Disease, parkinsonism, primary, otherextrapyramidal disease and abnormal movement disorders, otherdegenerative diseases of the basal ganglia, olivopontocerebellaratrophy, shydrager syndrome, essential tremor/familial tremor,myoclonus, lafora's disease, unverricht disease, Huntington's chorea,fragments of torsion dystonia, blepharospasm, other and unspecifiedextrapyramidal diseases and abnormal movement disorders, otherextrapyramidal diseases and abnormal movement disorders, restless legs,serotonin syndrome, spinocerebellar disease, friedreich's ataxia,spinocerebellar ataxia, hereditary spastic paraplegia, primarycerebellar degeneration, other cerebellar ataxia, cerebellar ataxia indiseases classified elsewhere, other spinocerebellar diseases, ataxiatelangiectasia, corticostriatal spinal degeneration, unspecifiedspinocerebellar disease, anterior horn cell disease, motor neurondisease, amyotrophic lateral sclerosis, progressive muscular atrophy,progressive bulbar palsy, pseudobulbar palsy, primary lateral sclerosis,other motor neuron diseases, other diseases of spinal cord,syringomyelia and syringobulbia, disorders of the autonomic nervoussystem, idiopathic peripheral autonomic neuropathy, unspecifiedidiopathic peripheral autonomic neuropathy, carotid sinus syndrome,other idiopathic peripheral autonomic neuropathy, peripheral autonomicneuropathy in disorders classified elsewhere, reflex sympatheticdystrophy, autonomic dysreflexia, and unspecified disorder of autonomicnervous system.

Pain includes, but is not limited to, central pain syndrome, acute pain,chronic pain, neoplasm related pain acute chronic and chronic painsyndrome. Other headache syndromes include, but are not limited tocluster headaches and other trigeminal autonomic cephalgias, unspecifiedcluster headache syndrome, episodic cluster headache, chronic clusterheadache, episodic paroxysmal hemicrania, chronic paroxysmal hemicrania,short lasting unilateral neuralgiform headache with conjunctivalinjection and tearing, other trigeminal autonomic cephalgias, tensiontype headache, unspecified tension type headache, episodic tension typeheadache, chronic tension type headache, post traumatic headache,unspecified post traumatic headache, acute post traumatic headache,chronic post traumatic headache, drug induced headache, not elsewhereclassified, complicated headache syndromes, hemicrania continua, newdaily persistent headache, primary thunderclap headache, othercomplicated headache syndrome, other specified headache syndromes,hypnic headache, headache associated with sexual activity, primary coughheadache, primary exertional headache, and primary stabbing headache.

Other disorders of the central nervous system include, but are notlimited to multiple sclerosis, other demyelinating diseases of centralnervous system, neuromyelitis optica, schilder's disease, acute myelitistransverse myelitis, hemiplegia, hemiplegia, flaccid, hemiplegia,spastic, infantile cerebral palsy, cerebral palsy, paraplegic,congenital, cerebral palsy, hemiplegic, congenital, cerebral palsy,quadriplegic, other paralytic syndromes, quadraplegia and quadraparesis,paraplegia, diplegia of upper limbs, monoplegia of lower limb,monoplegia of upper limb, unspecified monoplegia, cauda equina syndrome,other specified paralytic syndromes, locked in state, epilepsy,intractable epilepsy, tonic clonic epilepsy without status, epilepsywith status, epilepsy on temporal lobe without status, unspecifiedepilepsy without status, migraine, classical not intractable migraine,common but not intractable migraine, not intractable cluster headache,unspecified but, not intractable migraine, cataplexy and narcolepsy,narcolepsy without cataplexy, cerebral cysts, anoxic brain damage,pseudotumor cerebri, unspecified encephalopathy, metabolicencephalopathy, compression of brain, cerebral edema, post spinalpuncture, post dural puncture headache, cerebrospinal fluid rhinorrhea,and toxic encephalopathy.

Disorders of the peripheral nervous system include, but are not limitedto trigeminal nerve disorders, trigeminal neuralgia, facial nervedisorders, bell's palsy, disorders of other cranial nerves, nerve rootand plexus disorders, thoracic outlet syndrome, phantom limb,mononeuritis of upper limb and mononeuritis multiplex, carpal tunnel,mononeuritis of lower limb, lesion of sciatic nerve, meralgiaparesthetica, other lesion of femoral nerve, lesion of lateral poplitealnerve, lesion of medial popliteal nerve, tarsal tunnel syndrome, lesionof plantar nerve, morton's neuroma, unspecified mononeuritis of lowerlimb, mononeuritis of unspecified site, hereditary and idiopathicperipheral neuropathy, inflammatory and toxic neuropathy, guillain baryesyndrome, poly neuropathy, alcoholic poly neuropathy, myoneuraldisorders, myasthenia gravis with exacerbation, myasthenia graviswithout exacerbation, muscular dystrophies and other myopathies, benigncongenital myopathy, central core disease, centronuclear myopathy,myotubular myopathy, nemaline body disease, and hereditary musculardyst.

A biosignature of a vesicle can be assessed to provide a theranosis fora subject. The biosignature of the vesicle can comprise one or morebiomarkers such as, but not limited to, a biomarker such as thosedisclosed in the following table:

Neurology: Biosignature

A biosignature of a vesicle can be assessed to provide a theranosis fora subject. The biosignature of the vesicle can comprise one or morebiomarkers such as, but not limited to, a biomarker such as listed inFIGS. 1, 45, 46, 47, 48, and 49. The biosignature of the vesicle cancomprise one or more biomarkers including, but not limited to, amyloidβ, ICAM-1 (rodent), CGRP (rodent), TIMP-1 (rodent), CLR-1 (rodent),HSP-27 (rodent), FABP (rodent), ATP5B, ATP5H, ATP6V1B, DNM1, NDUFV2,NSF, PDHB, FGF2, ALDH7A1, AGXT2L1, AQP4, PCNT2, FGFR1, FGFR2, FGFR3,AQP4, a mutation of Dysbindin, DAOA/G30, DISC1, neuregulin-1, IFITM3,SERPINA3, GLS, ALDH7A1, BASP1, OX42, ED9, apolipoprotein D (rodent),miR-7, miR-24, miR-26b, miR-29b, miR-30b, miR-30e, miR-92, miR-195,miR-181b, DISC1, dysbindin, neuregulin-1, seratonin 2a receptor, andNURR1.

Neurology: Standard of Care

Determining the biosignature of a vesicle, the amount of vesicles, orboth, of a sample from a subject suffering from a neurological disorderor disease can be used to select a standard of care for the subject. Anneurological disorder or disease can be treated according to symptomsassociated with the condition. The standard of care can include, forexample, a pharmaceutical drug. A pharmaceutical drug includes, but isnot limited to aspirin, dipyridamole, naratriptan, apomorphine,donepezil, almotriptan malate, rufinamide, bromfenac, carbatrol,cenestin, tadalafil, clonazepam, entacapone, glatiramer acetate,pemoline, divalproex, difluprednate, zolpidem tartrate, rivastigminetartrate, dexmethylphenidate, frovatriptan succinate, zinc acetate,sumatriptan, paliperidone, iontocaine, morphine, levetiracetam,lamotrigine, vardenafil, lidocaine, eszopiclone, fospropofol disodium,pregabalin, rizatriptan benzoate, meropenem, Methylphenidate,dihydroergotamine mesylate, Pramipexole, rimabotulinumtoxin B,naltrexone, memantine, rotigotine, gabapentin), hydrocodone,mitoxantrone, armodafinil, oxycodone, pramipexole, samarium 153lexidronam, interferon beta-1a, dexfenfluramine, eletriptanhydrobromide, galantamine hydrobromide, ropinirole hydrochloride,riluzole, ramelteon, eldepryl, valproic acid, atomoxetine, tolcapone,carbamazepine, topiramate, oxcarbazepine, natalizumab, acetaminophen,tramadol, midazolam, lacosamide, iodixanol, lisdexamfetamine dimesylate,tetrabenazine, sodium oxybate, tizanidine hydrochloride, zolmitriptan,and zonisamide.

Other treatments that can be selected based on a vesicle profile of asubject includes those listed in Table 14, for a subject with MultipleSclerosis; Table 16, for a subject with Parkinson's Disease; or Table17, for a subject with depression.

TABLE 16 Classes of Drugs for Treatment of Parkinson's Disease ClassMechanism of Action Examples Dopamine Precursors Act as precursors inthe synthesis of dopamine, the Levodopa, neurotransmitter that isdepleted in Parkinson's Disease. Usually Levodopa- administered incombination with an inhibitor of the carboxylase carbidopa, enzyme thatmetabolizes levodopa. Some (e.g., Duodopa) are Levodopa- given byinfusion, e.g., intraduodenal infusion benserazide, Etilevodopa, DuodopaDopamine Agonists Mimic natural dopamine by directly stimulatingstriatal dopamine Bromocriptine, receptors. May be subclassed by whichof the five known Cabergoline, dopamine receptor subtypes the drugactivates; generally most Lisuride, Pergolide, effective are those thatactivate receptors the in the D2 receptor Pramipexole, family(specifically D2 and D3 receptors). Some are formulated Ropinirole, formore controlled release or transdermal delivery. Talipexole,Apomorphine, Dihydroergocryptine, Lisuride, Piribedil, Talipexole,Rotigotin CDS, Sumanirole, SLV- 308 COMT Inhibitors Inhibits COMT, thesecond major enzyme that metabolized Entacapone, levodopa. Tolcapone,Entacapone- Levodopa- Carbidopa fixed combination, MAO-B InhibitorsMAO-B metabolizes dopamine, and inhibitors of MAO-B thus Selegiline,prolong dopamine's half-life Rasagiline, Safinamide AntiglutamatergicBlock glutamate release. Reduce levodopa-induced dyskinesia Amantadine,Agents Budipine, Talampanel, Zonisamide Anticholinergic Thought toinhibit excessive cholinergic activity that Trihexyphenidyl, Agentsaccompanies dopamine deficiency Benztropine, Biperiden MixedDopaminergic Act on several neurotransmitter systems, both dopaminergicand NS-2330, Sarizotan Agents nondopaminergic. Adenosine A2a AdenosineA2 antagonize dopamine receptors and are found in Istradefyllineantagonists conjunction with dopamine receptors. Antagonists of thesereceptors may enhance the activity of dopamine receptors. Alpha-2Adrenergic Not known. Yohimbine, Antagonists Idazoxan, FipamezoleAntiapoptotic Agents Can slow the death of cells associated with theneurodegenerative CEP-1347, TCH- process of Parkinson's disease. 346Growth Factor Promote the survival and growth of dopaminergic cells.GPI-1485, Glial- Stimulators cell-line-derived Neurotrophic Factor,SR-57667, PYM-50028 Cell Replacement Replace damaged neurons with healthneurons. Spheramine Therapy

TABLE 17 Classes of Drugs for Treatment of Depression Class Mechanism ofAction Examples Selective Block presynaptic reuptake of serotonin. Exertlittle effect on Escitalopram, Sertraline, Serotonin norepinephrine ordopamine reuptake. Level of serotonin in Citalopram, Paroxetine,Reuptake the synaptic cleft is increased. Paroxetin, controlledInhibitor (SSRI) release, Fluoxetine, Fluoxetine weekly, Fluvoxamine,olanzapine/fluoxetine combination Serotonergic/ Inhibit both serotoninreuptake and norepinephrine reuptake. Venlafaxine; Reboxetine,noradrenergic Different drugs in this class can inhibit each receptor toMilnacipran, Mirtazapine, agents different degrees. Do not affecthistamine, acetylcholine, and Nefazodone, Duloxetine adrenergicreceptors. Serotonergic/noradrenergic/ Several different mechanisms.Block norepinephrine, Bupropion, Maprotiline, dopaminergic serotonin,and/or dopamine reuptake. Some have addictive Mianserin, Trazodone,agents potential due to dopamine reuptake inhibition.Dexmethylphenidate, Methyphenidate, Amineptine Tricyclic Block synapticreuptake of serotonin and norepinephrine. Amitriptyline, Amoxapine,Antidepressants Have little effect on dopamine. Strong blockers ofmuscarinic, Clomipramine, histaminergic H1, and alpha-1-adrenergicreceptors. Desipramine, Doxepin, Imipramine, Nortriptyline,Protriptyline, Trimipramine Irreversible Monoamine oxidase (MAO)metabolizes monoamines such as Isocarboxazid, Phenelzine, Monoamineserotonin and norepinephrine. MAO inhibitors inhibit MAO,Tranylcypromine, Oxidase thus increasing levels of serotonin andnorepinephrine. Transdermal Selegiline Inhibitors Reversible See above.Short acting, reversible inhibitor, inhibits Moclobemide Monoaminedeamination of serotonin, norepinephrine, and dopamine. OxidaseInhibitors Serotonergic/noradrenergic/ Act to block all of serotonin,norepinephrine, and dopamine DOV-216303, DOV-21947 dopaminergicreuptake. May have addictive potential due to dopamine reuptake reuptakeinhibition. inhibitors Noradrenergic/dopaminergic Block reuptake ofnorepinephrine and dopamine GW-353162 agents Serotonin Selectiveantagonist of one serotonin receptor (the 5-HT₁ Agomelatine Antagonistsreceptor) Serotonin Partial agonist of the 5-HT_(1A) receptor.Eptapirone, Vilazodone, Agonists OPC-14523, MKC-242, Gepirone ERSubstance P Modify levels of substance P, which is released during acuteAprepitant, TAK-637, CP- Antagonists stress. 122721, E6006, R-763OPC-GW-597599 Beta₃ Indirectly inhibit norepinephrine reuptake. Also beingSR-58611 Adrenoreceptor investigated for treatment of obesity anddiabetes because Agonists they stimulate lipolysis and thermogenesis.

In one embodiment, a treatment can be selected for a subject sufferingfrom Alzheimer's disease. One or more biomarkers, such as, but notlimited to, beta-amyloid protein, amyloid precursor protein (APP),APP670/671, APP693, APP692, APP715, APP716, APP717, APP723, presenilin1, presenilin 2, cerebrospinal fluid amyloid beta protein 42(CSF-Abeta42), cerebrospinal fluid amyloid beta protein 40(CSF-Abeta40), F2 isoprostane, 4-hydroxynonenal, F4 neuroprostane, andacrolein, can be assessed from a vesicle from the subject. Based on oneor more characteristics of the one or more biomarkers, the subject canbe determined to be a responder or non-responder for a treatment, suchas, but not limited to, Donepezil, Galantamine, Memantine, Rivastigmine,Tacrine, or a combination thereof.

In another embodiment, a treatment can be selected for a subjectsuffering from Parkinson's Disease. One or more biomarkers, such as, butnot limited to, alpha synuclein, PARK7 (DJ-1), S-phase kinase-associatedprotein 1A (p19A/SKP1A), Heat shock protein 70 kDa, AMP-regulatedphosphoprotein (ARPP-21), vesicular monoamine member 2 (VMAT2), alcoholdehydrogenase 5 (ADH5), aldehyde dehydrogenase 1A1 (ALDH1A1), egle ninehomolog 1(EGLN1), proline hydroxylase 2 (PHD2), and hypoxia induciblefactor (HIF), can be assessed from a vesicle from the subject. Based onone or more characteristics of the one or more biomarkers, the subjectcan be determined to be a responder or non-responder for a treatment,such as, but not limited to, those listed in Table 16.

In another embodiment, a treatment can be selected for a subjectsuffering from Parkinson's Disease. One or more biomarkers, such as, butnot limited to, CRP, TNF, IL-6, S100B, and MMP can be assessed from avesicle from the subject. Based on one or more characteristics of theone or more biomarkers, the subject can be determined to be a responderor non-responder for a treatment.

Thus, a treatment can be selected for the subject suffering from aneurology-related condition or neurological condition or disease, basedon the biosignature of the subject's vesicle.

Biosignature Discovery

The systems and methods provided herein can be used in identifying anovel biosignature of a vesicle, such as one or more novel biomarkersfor the diagnosis, prognosis or theranosis of a phenotype. In oneembodiment, one or more vesicles can be isolated from a subject with aphenotype and a biosignature of the one or more vesicles determined. Thebiosignature can be compared to a subject without the phenotype.Differences between the two biosignatures can be determined and used toform a novel biosignature. The novel biosignature can then be used foridentifying another subject as having the phenotype or not having thephenotype.

Differences between the biosignature from a subject with a particularphenotype can be compared to the biosignature from a subject without theparticular phenotype. The one or more differences can be a difference inany characteristic of the vesicle. For example, the level or amount ofvesicles in the sample, the half-life of the vesicle, the circulatinghalf-life of the vesicle, the metabolic half-life of the vesicle, or theactivity of the vesicle, or any combination thereof, can differ betweenthe biosignature from the subject with a particular phenotype and thebiosignature from the subject without the particular phenotype.

In some embodiments, one or more biomarkers differ between thebiosignature from the subject with a particular phenotype and thebiosignature from the subject without the particular phenotype. Forexample, the expression level, presence, absence, mutation, variant,copy number variation, truncation, duplication, modification, molecularassociation of one or more biomarkers, or any combination thereof, maydiffer between the biosignature from the subject with a particularphenotype and the biosignature from the subject without the particularphenotype. The biomarker can be any biomarker disclosed herein or thatcan be used to characterize a biological entity, including a circulatingbiomarker, such as protein or microRNA, a vesicle, or a componentpresent in a vesicle or on the vesicle, such as any nucleic acid (e.g.RNA or DNA), protein, peptide, polypeptide, antigen, lipid,carbohydrate, or proteoglycan.

In an aspect, the invention provides a method of discovering a novelbiosignature comprising comparing the biomarkers between two or moresample groups to identify biomarkers that show a difference between thesample groups. Multiple markers can be assessed in a panel format topotentially improve the performance of individual markers. In someembodiments, the multiple markers are assessed in a multiplex fashion.The ability of the individual markers and groups of markers todistinguish the groups can be assessed using statistical discriminateanalysis or classification methods as used herein. Optimal panels ofmarkers can be used as a biosignature to characterize the phenotypeunder analysis, such as to provide a diagnosis, prognosis or theranosisof a disease or condition. Optimization can be based on variouscriteria, including without limitation maximizing ROC AUC, accuracy,sensitivity at a certain specificity, or specificity at a certainsensitivity. The panels can include biomarkers from multiple types. Forexample, the biosignature can comprise vesicle antigens useful forcapturing a vesicle population of interest, and the biosignature canfurther comprise payload markers within the vesicle population,including without limitation microRNAs, mRNAs, or soluble proteins.Optimal combinations can be identified as those vesicle antigens andpayload markers with the greatest ROC AUC value when comparing twosettings. As another example, the biosignature can be determined byassessing a vesicle population in addition to assessing circulatingbiomarkers that are not obtained by isolating exosomes, such ascirculating proteins and/or microRNAs.

The phenotype can be any of those listed herein, e.g., in the“Phenotype” section above. For example, the phenotype can be aproliferative disorder such as a cancer or non-malignant growth, aperinatal or pregnancy related condition, an infectious disease, aneurological disorder, a cardiovascular disease, an inflammatorydisease, an immune disease, or an autoimmune disease. The cancerincludes without limitation lung cancer, non-small cell lung cancermsmall cell lung cancer (including small cell carcinoma (oat cellcancer), mixed small cell/large cell carcinoma, and combined small cellcarcinoma), colon cancer, breast cancer, prostate cancer, liver cancer,pancreatic cancer, brain cancer, kidney cancer, ovarian cancer, stomachcancer, melanoma, bone cancer, gastric cancer, breast cancer, glioma,gliobastoma, hepatocellular carcinoma, papillary renal carcinoma, headand neck squamous cell carcinoma, leukemia, lymphoma, myeloma, or othersolid tumors.

Any of the types of biomarkers or specific biomarkers described hereincan be assessed to discover a novel biosignature. In an embodiment, thebiomarkers selected for discovery comprise cell-specific biomarkers aslisted herein, including without limitation the genes and microRNAlisted in FIGS. 1-60, Tables 9-11 or Tables 27-41. The biomarkers cancomprise one or more drug associated target such as a ABCC1, ABCG2,ACE2, ADA, ADH1C, ADH4, AGT, AR, AREG, ASNS, BCL2, BCRP, BDCA1, beta IIItubulin, BIRC5, B-RAF, BRCA1, BRCA2, CA2, caveolin, CD20, CD25, CD33,CD52, CDA, CDKN2A, CDKN1A, CDKN1B, CDK2, CDW52, CES2, CK 14, CK 17, CK5/6, c-KIT, c-Met, c-Myc, COX-2, Cyclin D1, DCK, DHFR, DNMT1, DNMT3A,DNMT3B, E-Cadherin, ECGF1, EGFR, EML4-ALK fusion, EPHA2, Epiregulin, ER,ERBR2, ERCC1, ERCC3, EREG, ESR1, FLT1, folate receptor, FOLR1, FOLR2,FSHB, FSHPRH1, FSHR, FYN, GART, GNRH1, GNRHR1, GSTP1, HCK, HDAC1,hENT-1, Her2/Neu, HGF, HIF1A, HIG1, HSP90, HSP90AA1, HSPCA, IGF-1R,IGFRBP, IGFRBP3, IGFRBP4, IGFRBP5, IL13RA1, IL2RA, KDR, Ki67, KIT,K-RAS, LCK, LTB, Lymphotoxin Beta Receptor, LYN, MET, MGMT, MLH1, MMR,MRP1, MS4A1, MSH2, MSH5, Myc, NFKB1, NFKB2, NFKBIA, ODC1, OGFR, p16,p21, p27, p53, p95, PARP-1, PDGFC, PDGFR, PDGFRA, PDGFRB, PGP, PGR,PI3K, POLA, POLA1, PPARG, PPARGC1, PR, PTEN, PTGS2, RAF1, RARA, RRM1,RRM2, RRM2B, RXRB, RXRG, SIK2, SPARC, SRC, SSTR1, SSTR2, SSTR3, SSTR4,SSTR5, Survivin, TK1, TLE3, TNF, TOP1, TOP2A, TOP2B, TS, TXN, TXNRD1,TYMS, VDR, VEGF, VEGFA, VEGFC, VHL, YES1, and ZAP70. The biomarkers cancomprise one or more general vesicle marker, one or more cell-specificvesicle marker, and/or one or more disease-specific vesicle marker.

The biomarkers used for biosignature discovery can comprise includemarkers commonly associated with vesicles, including without limitationone or more of HSPA8, CD63, Actb, GAPDH, CD9, CD81, ANXA2, HSP90AA1,ENO1, YWHAZ, PDCD6IP, CFL1, SDCBP, PKN2, MSN, MFGE8, EZR, YWHAG, PGK1,EEF1A1, PPIA, GLC1F, GK, ANXA6, ANXA1, ALDOA, ACTG1, TPI1, LAMP2,HSP90AB1, DPP4, YWHAB, TSG101, PFN1, LDHB, HSPA1B, HSPA1A, GSTP1, GNAI2,GDI2, CLTC, ANXA5, YWHAQ, TUBA1A, THBS1, PRDX1, LDHA, LAMP1, CLU, andCD86. The biomarkers can further comprise one or more of CD63, GAPDH,CD9, CD81, ANXA2, ENO1, SDCBP, MSN, MFGE8, EZR, GK, ANXA1, LAMP2, DPP4,TSG101, HSPA1A, GDI2, CLTC, LAMP1, Cd86, ANPEP, TFRC, SLC3A2, RDX,RAP1B, RAB5C, RAB5B, MYH9, ICAM1, FN1, RAB11B, PIGR, LGALS3, ITGB1,EHD1, CLIC1, ATP1A1, ARF1, RAP1A, P4HB, MUC1, KRT10, HLA-A, FLOT1, CD59,C1orf58, BASP1, TACSTD1, and STOM. Other biomarkers can be selected fromthose disclosed in the ExoCarta database, available atexocarta.ludwig.edu.au, which discloses proteins and RNA moleculesidentified in exosomes. See also Mathivanan and Simpson, ExoCarta: Acompendium of exosomal proteins and RNA. Proteomics. 2009 November9(21):4997-5000.

The biomarkers used for biosignature discovery can comprise includemarkers commonly associated with vesicles, including without limitationone or more of A33, a33 n15, AFP, ALA, ALIX, ALP, AnnexinV, APC, ASCA,ASPH (246-260), ASPH (666-680), ASPH (A-10), ASPH (D01P), ASPH (D03),ASPH (G-20), ASPH(H-300), AURKA, AURKB, B7H3, B7H4, BCA-225, BCNP1,BDNF, BRCA, CA125 (MUC16), CA-19-9, C-Bir, CD1.1, CD10, CD174 (Lewis y),CD24, CD44, CD46, CD59 (MEM-43), CD63, CD66e CEA, CD73, CD81, CD9, CDA,CDAC1 1a2, CEA, C-Erb2, C-erbB2, CRMP-2, CRP, CXCL12, CYFRA21-1, DLL4,DR3, EGFR, Epcam, EphA2, EphA2 (H-77), ER, ErbB4, EZH2, FASL, FRT, FRTc.f23, GDF15, GPCR, GPR30, Gro-alpha, HAP, HBD 1, HBD2, HER 3 (ErbB3),HSP, HSP70, hVEGFR2, iC3b, IL 6 Unc, IL-1B, IL6 Unc, IL6R, IL8, IL-8,INSIG-2, KLK2, L1CAM, LAMN, LDH, MACC-1, MAPK4, MART-1, MCP-1, M-CSF,MFG-E8, MIC1, MIF, MIS RII, MMG, MMP26, MMP7, MMP9, MS4A1, MUC1, MUC1seq1, MUC1 seq11A, MUC17, MUC2, Ncam, NGAL, NPGP/NPFF2, OPG, OPN, p53,p53, PA2G4, PBP, PCSA, PDGFRB, PGP9.5, PIM1, PR (B), PRL, PSA, PSMA,PSME3, PTEN, R5-CD9 Tube 1, Reg IV, RUNX2, SCRN1, seprase, SERPINB3,SPARC, SPB, SPDEF, SRVN, STAT 3, STEAP1, TF (FL-295), TFF3, TGM2,TIMP-1, TIMP1, TIMP2, TMEM211, TMPRSS2, TNF-alpha, Trail-R2, Trail-R4,TrKB, TROP2, Tsg 101, TWEAK, UNC93A, VEGF A, and YPSMA-1. The biomarkerscan include one or more of NSE, TRIM29, CD63, CD151, ASPH, LAMP2,TSPAN1, SNAIL, CD45, CKS1, NSE, FSHR, OPN, FTH1, PGP9, ANNEXIN 1, SPD,CD81, EPCAM, PTH1R, CEA, CYTO 7, CCL2, SPA, KRAS, TWIST1, AURKB, MMP9,P27, MMP1, HLA, HIF, CEACAM, CENPH, BTUB, INTG b4, EGFR, NACC1, CYTO 18,NAP2, CYTO 19, ANNEXIN V, TGM2, ERB2, BRCA1, B7H3, SFTPC, PNT, NCAM,MS4A1, P53, INGA3, MUC2, SPA, OPN, CD63, CD9, MUC1, UNCR3, PAN ADH, HCG,TIMP, PSMA, GPCR, RACK1, PCSA, VEGF, BMP2, CD81, CRP, PRO GRP, B7H3,MUC1, M2PK, CD9, PCSA, and PSMA. The biomarkers can also include one ormore of TFF3, MS4A1, EphA2, GAL3, EGFR, N-gal, PCSA, CD63, MUC1, TGM2,CD81, DR3, MACC-1, TrKB, CD24, TIMP-1, A33, CD66 CEA, PRL, MMP9, MMP7,TMEM211, SCRN1, TROP2, TWEAK, CDACC1, UNC93A, APC, C-Erb, CD10, BDNF,FRT, GPR30, P53, SPR, OPN, MUC2, GRO-1, tsg 101 and GDF15. Inembodiments, the biomarkers used to discover a biosignature comprise oneor more of those shown in FIGS. 99, 100, 108A-C, 114A, and/or 115A-E.

One of skill will appreciate that any marker disclosed herein or thatcan be compared between two samples or sample groups of interest can beused to discover a novel biosignature for any given biological settingthat can be compared.

The one or more differences can then be used to form a novelbiosignature for the particular phenotype, such as the diagnosis of acondition, diagnosis of a stage of a disease or condition, prognosis ofa condition, or theranosis of a condition. The novel biosignature canthen be used to identify the phenotype in other subjects. Thebiosignature of a vesicle for a new subject can be determined andcompared to the novel signature to determine if the subject has theparticular phenotype for which the novel biosignature was identifiedfrom.

For example, the biosignature of a subject with cancer can be comparedto another subject without cancer. Any differences can be used to form anovel biosignature for the diagnosis of the cancer. In anotherembodiment, the biosignature of a subject with an advanced stage ofcancer can be compared to another subject with a less advanced stage ofcancer. Any differences can be used to form a novel biosignature for theclassification of the stage of cancer. In yet another embodiment, thebiosignature of a subject with an advanced stage of cancer can becompared to another subject with a less advanced stage of cancer. Anydifferences can be used to form a novel biosignature for theclassification of the stage of cancer.

In one embodiment, the phenotype is drug resistance ornon-responsiveness to a therapeutic. One or more vesicles can beisolated from a non-responder to a particular treatment and thebiosignature of the vesicle determined. The biosignature of the vesicleobtained from the non-responsder can be compared to the biosignature ofa vesicle obtained from a responsder. Differences between thebiosignature from the non-responder can be compared to the biosignaturefrom the responder. The one or more differences can be a difference inany characteristic of the vesicle. For example, the level or amount ofvesicles in the sample, the half-life of the vesicle, the circulatinghalf-life of the vesicle, the metabolic half-life of the vesicle, theactivity of the vesicle, or any combination thereof, can differ betweenthe biosignature from the non-responder and the biosignature from theresponder.

In some embodiments, one or more biomarkers differ between thebiosignature from the non-responder and the biosignature from theresponder. For example, the expression level, presence, absence,mutation, variant, copy number variation, truncation, duplication,modification, molecular association of one or more biomarkers, or anycombination thereof, may differ between the biosignature from thenon-responder and the biosignature from the responder.

In some embodiments, the difference can be in the amount of drug or drugmetabolite present in the vesicle. Both the responder and non-respondercan be treated with a therapeutic. A comparison between the biosignaturefrom the responder and the biosignature from the non-responder can beperformed, the amount of drug or drug metabolite present in the vesiclefrom the responder differs from the amount of drug or drug metabolitepresent in the non-responder. The difference can also be in thehalf-life of the drug or drug metabolite. A difference in the amount orhalf-life of the drug or drug metabolite can be used to form a novelbiosignature for identifying non-responders and responders.

A vesicle useful for methods and compositions described herein can bediscovered by taking advantage of its physicochemical characteristics.For example, a vesicle can be discovered by its size, e.g., by filteringbiological matter in a known range from 30-120 nm in diameter.Size-based discovery methods, such as differential centrifugation,sucrose gradient centrifugation, or filtration have been used forisolation of a vesicle.

A vesicle can be discovered by its molecular components. Molecularproperty-based discovery methods include, but are not limited to,immunological isolation using antibodies recognizing moleculesassociated with vesicle. For example, a surface molecule associated witha vesicle includes, but not limited to, a MHC-II molecule, CD63, CD81,LAMP-1, Rab7 or Rab5.

Various techniques known in the art are applicable for validation andcharacterization of a vesicle. Techniques useful for validation andcharacterization of a vesicle includes, but is not limited to, westernblot, electron microscopy, immunohistochemistry, immunoelectronmicroscopy, FACS (Fluorescent activated cell sorting), electrophoresis(1 dimension, 2 dimension), liquid chromatography, mass spectrometry,MALDI-TOF (matrix assisted laser desorption/ionization-time of flight),ELISA, LC-MS-MS, and nESI (nanoelectrospray ionization). For exampleU.S. Pat. No. 2009/0148460 describes use of an ELISA method tocharacterize a vesicle. U.S. Pat. No. 2009/0258379 describes isolationof membrane vesicles from biological fluids.

Vesicles can be further analyzed for one or more nucleic acids, lipids,proteins or polypeptides, such as surface proteins or peptides, orproteins or peptides within a vesicle. Candidate peptides can beidentified by various techniques including mass spectrometry coupledwith purification methods such as liquid chromatography. A peptide canthen be isolated and its sequence can be identified by sequencing. Acomputer program that predicts a sequence based on exact mass of apeptide can also be used to reveal the sequence of a peptide isolatedfrom a vesicle. For example, LTQ-Orbitrap mass spectrometry can be usedfor high sensitivity and high accuracy peptide sequencing. LTQ-Orbitrapmethod has been described (Simpson et al, Expert Rev. Proteomics6:267-283, 2009), which is incorporated herein by reference in itsentirety.

Vesicle Compositions

Also provided herein is an isolated vesicle with a particularbiosignature. The isolated vesicle can comprise one or more biomarkersor biosignatures specific for specific cell type, or for characterizinga phenotype, such as described above. For example, the isolated vesiclecan comprise one or more biomarkers, such as CD63, EpCam, CD81, CD9,PCSA, PSMA, B7H3, TNFR, MFG-E8, Rab, STEAP, 5T4, or CD59. The isolatedvesicle can comprise one or more of the following biomarkers: EpCam,CD9, PCSA, CD63, CD81, PSMA, B7H3, PSCA, ICAM, STEAP, and EGFR. In oneembodiment, the vesicle is EpCam+, CK+, CD45−. The isolated vesicle canhave the one or more biomarkers on its surface or within the vesicle.The isolated vesicle can also comprise one or more miRNAs, such asmiR-9, miR-629, miR-141, miR-671-3p, miR-491, miR-182, miR-125a-3p,miR-324-5p, miR-148B, or miR-222. In one embodiment, the vesiclecomprises one or more miRNAs, such as miR-548c-5p, miR-362-3p, miR-422a,miR-597, miR-429, miR-200a, and miR-200b. In yet another embodiment, thevesicle comprises one or more miRNAs, such as miR-92a-2*, miR-147,miR-574-5p. An isolated vesicle can comprise a biomarker such as CD66,and further comprise one or more biomarkers selected from the groupconsisting of: EpCam, CD63, or CD9. An isolated vesicle can alsocomprise a fusion gene or protein, such as TMRSSG2:ERG.

An isolated vesicle can also comprise one or more biomarkers, whereinthe expression level of the one or more biomarkers is higher, lower, orthe same for an isolated vesicle as compared to an isolated vesiclederived from a normal cell (ie. a cell derived from a subject without aphenotype of interest). For example, an isolated vesicle can compriseone or more biomarkers selected from the group consisting of: B7H3,PSCA, MFG-E8, Rab, STEAP, PSMA, PCSA, 5T4, miR-9, miR-629, miR-141,miR-671-3p, miR-491, miR-182, miR-125a-3p, miR-324-5p, miR-148b, andmiR-222, wherein the expression level of the one or more biomarkers ishigher for an isolated vesicle as compared those derived from a normalcell. The isolated vesicle can comprise at least 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 13, 14, 15, 16, 17, 18, or 19 of the biomarkers selected fromthe group. The isolated vesicle can further comprising one or morebiomarkers selected from the group consisting of: EpCam, CD63, CD59,CD81, or CD9.

An isolated vesicle can comprise the biomarkers PCSA, EpCam, CD63, andCD8; the biomarkers PCSA, EpCam, B7H3 and PSMA. An isolated vesicle cancomprise the biomarkers miR-9, miR-629, miR-141, miR-671-3p, miR-491,miR-182, miR-125a-3p, miR-324-5p, miR-148b, and miR-222.

A composition comprising an isolated vesicle is also provided herein.The composition can comprise one or more isolated vesicles. For example,the composition can comprise a plurality of vesicles, or one or morepopulations of vesicles.

The composition can be substantially enriched for vesicles. For example,the composition can be substantially absent of cellular debris, cells,or non-exosomal proteins, peptides, or nucleic acids (such as biologicalmolecules not contained within the vesicles). The cellular debris,cells, or non-exosomal proteins, peptides, or nucleic acids, can bepresent in a biological sample along with vesicles. A composition can besubstantially absent of cellular debris, cells, or non-exosomalproteins, peptides, or nucleic acids (such as biological molecules notcontained within the vesicles), can be obtained by any method disclosedherein, such as through the use of one or more binding agents or captureagents for one or more vesicles. The vesicles can comprise at least 30,40, 50, 60, 70, 80, 90, 95 or 99% of the total composition, by weight orby mass. The vesicles of the composition can be a heterogeneous orhomogeneous population of vesicles. For example, a homogeneouspopulation of vesicles comprises vesicles that are homogeneous as to oneor more properties or characteristics. For example, the one or morecharacteristics can be selected from a group consisting of: one or moreof the same biomarkers, a substantially similar or identicalbiosignature, derived from the same cell type, vesicles of a particularsize, and a combination thereof.

Thus, in some embodiments, the composition comprises a substantiallyenriched population of vesicles. The composition can be enriched for apopulation of vesicles that are at least 30, 40, 50, 60, 70, 80, 90, 95or 99% homogeneous as to one or more properties or characteristics. Forexample, the one or more characteristics can be selected from a groupconsisting of: one or more of the same biomarkers, a substantiallysimilar or identical biosignature, derived from the same cell type,vesicles of a particular size, and a combination thereof. For example,the population of vesicles can be homogeneous by all having a particularbiosignature, having the same biomarker, having the same biomarkercombination, or derived from the same cell type. In some embodiments,the composition comprises a substantially homogeneous population ofvesicles, such as a population with a specific biosignature, derivedfrom a specific cell, or both.

The population of vesicles can comprise one or more of the samebiomarkers. The biomarker can be any component such as any nucleic acid(e.g. RNA or DNA), protein, peptide, polypeptide, antigen, lipid,carbohydrate, or proteoglycan. For example, each vesicle in a populationcan comprise the same or identical one or more biomarkers. In someembodiments, each vesicle comprises the same 1, 2, 3, 4, 5, 6, 3, 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 50, 75 or100 biomarkers. The one or more biomarkers can be selected from FIGS. 1,3-60.

The vesicle population comprising the same or identical biomarker canrefer to each vesicle in the population having the same presence orabsence, expression level, mutational state, or modification of thebiomarker. For example, an enriched population of vesicle can comprisevesicles wherein each vesicle has the same biomarker present, the samebiomarker absent, the same expression level of a biomarker, the samemodification of a biomarker, or the same mutation of a biomarker. Thesame expression level of a biomarker can refer to a quantitative orqualitive measurement, such as the vesicles in the populationunderexpress, overexpress, or have the same expression level of abiomarker as compared to a reference level.

Alternatively, the same expression level of a biomarker can be anumerical value representing the expression of a biomarker that issimilar for each vesicle in a population. For example the copy number ofa miRNA, the amount of protein, or the level of mRNA of each vesicle,can be quantitatively similar for each vesicle in a population, suchthat the numerical amount of each vesicle is ±1, 2, 3, 4, 5, 6, 7, 8, 9,10, 15, or 20% from the amount in each other vesicle in the population,as such variations are appropriate.

In some embodiments, the composition comprises a substantially enrichedpopulation of vesicles, wherein the vesicles in the enriched populationhas a substantially similar or identical biosignature. The biosignaturecan comprise one or more characteristic of the vesicle, such as thelevel or amount of vesicles, temporal evaluation of the variation invesicle half-life, circulating vesicle half-life, metabolic half-life ofa vesicle, or the activity of a vesicle. The biosignature can alsocomprise the presence or absence, expression level, mutational state, ormodification of a biomarker, such as those described herein.

The biosignature of each vesicle in the population can be at least 30,40, 50, 60, 70, 80, 90, 95, or 99% identical. In some embodiments, thebiosignature of each vesicle is 100% identical. The biosignature of eachvesicle in the enriched population can have the same 1, 2, 3, 4, 5, 6,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 50,75 or 100 characteristics. For example, a biosignature of a vesicle inan enriched population can be the presence of a first biomarker, thepresence of a second biomarker, and the underexpression of a thirdbiomarker. Another vesicle in the same population can be 100% identical,having the same first and second biomarkers present and underexpressionof the third biomarker. Alternatively, a vesicle in the same populationcan have the same first and second biomarkers, but not haveunderexpression of the third biomarker.

In some embodiments, the composition comprises a substantially enrichedpopulation of vesicles, wherein the vesicles are derived from the samecell type. For example, the vesicles can all be derived from cells of aspecific tissue, cells from a specific tumor of interest or a diseasedtissue of interest, circulating tumor cells, or cells of maternal orfetal origin. The vesicles can all be derived from tumor cells. Thevesicles can all be derived from lung, pancreas, stomach, intestine,bladder, kidney, ovary, testis, skin, colorectal, breast, prostate,brain, esophagus, liver, placenta, or fetal cells.

The composition comprising a substantially enriched population ofvesicles can also comprise vesicles are of a particular size. Forexample, the vesicles can all a diameter of greater than about 10, 20,or nm. They can all have a diameter of about 30-1000 nm, about 30-800nm, about 30-200 nm, or about 30-100 nm. In some embodiments, thevesicles can all have a diameter of less than about 10,000 nm, 1000 nm,800 nm, 500 nm, 200 nm, 100 nm or 50 nm.

The population of vesicles homogeneous for one or more characteristicscan comprises at least about 30, 40, 50, 60, 70, 80, 90, 95, or 99% ofthe total vesicle population of the composition. In some embodiments, acomposition comprising a substantially enriched population of vesiclescomprises at least 2, 3, 4, 5, 10, 20, 25, 50, 100, 250, 500, or 1000times the concentration of vesicle as compared to a concentration of thevesicle in a biological sample from which the composition was derived.In yet other embodiments, the composition can further comprise a secondenriched population of vesicles, wherein the population of vesicles isat least 30% homogeneous as to one or more characteristics, as describedherein.

Multiplex analysis can be used to obtain a composition substantiallyenriched for more than one population of vesicles, such as at least 2,3, 4, 5, 6, 7, 8, 9, 10 vesicle, populations. Each substantiallyenriched vesicle population can comprise at least 5, 10, 15, 20, 25, 30,35, 40, 45, 46, 47, 48, or 49% of the composition, by weight or by mass.In some embodiments, the substantially enriched vesicle populationcomprises at least about 30, 40, 50, 60, 70, 80, 90, 95, or 99% of thecomposition, by weight or by mass.

A substantially enriched population of vesicles can be obtained by usingone or more methods, processes, or systems as disclosed herein. Forexample, isolation of a population of vesicles from a sample can beperformed by using one or more binding agents for one or more biomarkersof a vesicle, such as using two or more binding agents that target twoor more biomarkers of a vesicle. One or more capture agents can be usedto obtain a substantially enriched population of vesicles. One or moredetection agents can be used to identify a substantially enrichedpopulation of vesicles.

In one embodiment, a population of vesicles with a particularbiosignature is obtained by using one or more binding agents for thebiomarkers of the biosignature. The vesicles can be isolated resultingin a composition comprising a substantially enriched population ofvesicles with the particular biosignature. In another embodiment, apopulation of vesicles with a particular biosignature of interest can beobtained by using one or more binding agents for biomarkers that are nota component of the biosignature of interest. Thus, the binding agentscan be used to remove the vesicles that do not have the biosignature ofinterest and the resulting composition is substantially enriched for thepopulation of vesicles with the particular biosignature of interest. Theresulting composition can be substantially absent of the vesiclescomprising a biomarker for the binding agent.

Detection System and Kits

Also provided is a detection system configured to determine one or morebiosignatures for a vesicle. The detection system can be used to detecta heterogeneous population of vesicles or one or more homogeneouspopulation of vesicles. The detection system can be configured to detecta plurality of vesicles, wherein at least a subset of the plurality ofvesicles comprises a different biosignature from another subset of theplurality of vesicles. The detection system detect at least 2, 3, 4, 5,6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, or 100 differentsubsets of vesicles, wherein each subset of vesicles comprises adifferent biosignature. For example, a detection system, such as usingone or more methods, processes, and compositions disclosed herein, canbe used to detect at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30,40, 50, 60, 70, 80, 90, or 100 different populations of vesicles.

The detection system can be configured to assess at least 2, 3, 4, 5, 6,7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 1000, 2500,5000, 7500, 10,000, 100,000, 150,000, 200,000, 250,000, 300,000,350,000, 400,000, 450,000, 500,000, 750,000, or 1,000,000 differentbiomarkers for one or more vesicles. In some embodiments, the one ormore biomarkers are selected from FIGS. 1, 3-60, or as disclosed herein.The detection system can be configured to assess a specific populationof vesicles, such as vesicles from a specific cell-of-origin, or toassess a plurality of specific populations of vesicles, wherein eachpopulation of vesicles has a specific biosignature.

The detection system can be a low density detection system or a highdensity detection system. For example, a low density detection systemcan detect up to 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 different vesiclepopulations, whereas a high density detection system can detect at leastabout 15, 20, 25, 50, or 100 different vesicle populations. In anotherembodiment, a low density detection system can detect up to about 100,200, 300, 400, or 500 different biomarkers, whereas a high densitydetection system can detect at least about 750, 1000, 2000, 3000, 4000,5000, 6000, 7000, 8000, 9,000, 10,000, 15,000, 20,000, 25,000, 50,000,or 100,000 different biomarkers. In yet another embodiment, a lowdensity detection system can detect up to about 100, 200, 300, 400, or500 different biosignatures or biomarker combinations, whereas a highdensity detection system can detect at least about 750, 1000, 2000,3000, 4000, 5000, 6000, 7000, 8000, 9,000, 10,000, 15,000, 20,000,25,000, 50,000, or 100,000 biosignatures or biomarker combinations.

The detection system can comprise a probe that selectively hybridizes toa vesicle. The detection system can comprise a plurality of probes todetect a vesicle. In some embodiments, a plurality of probes is used todetect the amount of vesicles in a heterogeneous population of vesicles.In yet other embodiments, a plurality of probes is used to detect ahomogeneous population of vesicles. A plurality of probes can be used toisolate or detect at least two different subsets of vesicles, whereineach subset of vesicles comprises a different biosignature.

A detection system, such as using one or more methods, processes, andcompositions disclosed herein, can comprise a plurality of probesconfigured to detect, or isolate, such as using one or more methods,processes, and compositions disclosed herein at least 2, 3, 4, 5, 6, 7,8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, or 100 differentsubsets of vesicles, wherein each subset of vesicles comprises adifferent biosignature.

For example, a detection system can comprise a plurality of probesconfigured to detect at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25,30, 40, 50, 60, 70, 80, 90, or 100 different populations of vesicles.The detection system can comprise a plurality of probes configured toselectively hybridize to at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20,25, 30, 40, 50, 60, 70, 80, 90, 100, 1000, 2500, 5000, 7500, 10,000,100,000, 150,000, 200,000, 250,000, 300,000, 350,000, 400,000, 450,000,500,000, 750,000, or 1,000,000 different biomarkers for one or morevesicles. In some embodiments, the one or more biomarkers are selectedfrom FIGS. 1, 3-60, or as disclosed herein. The plurality of probes canbe configured to assess a specific population of vesicles, such asvesicles from a specific cell-of-origin, or to assess a plurality ofspecific populations of vesicles, wherein each population of vesicleshas a specific biosignature.

The detection system can be a low density detection system or a highdensity detection system comprising probes to detect vesicles. Forexample, a low density detection system can comprise probes to detect upto 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 different vesicle populations,whereas a high density detection system can comprise probes to detect atleast about 15, 20, 25, 50, or 100 different vesicle populations. Inanother embodiment, a low density detection system can comprise probesto detect up to about 100, 200, 300, 400, or 500 different biomarkers,whereas a high density detection system can comprise probes to detect atleast about 750, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9,000,10,000, 15,000, 20,000, 25,000, 50,000, or 100,000 different biomarkers.In yet another embodiment, a low density detection system can compriseprobes to detect up to about 100, 200, 300, 400, or 500 differentbiosignatures or biomarker combinations, whereas a high densitydetection system can comprise probes to detect at least about 750, 1000,2000, 3000, 4000, 5000, 6000, 7000, 8000, 9,000, 10,000, 15,000, 20,000,25,000, 50,000, or 100,000 biosignatures or biomarker combinations.

The probes can be specific for detecting a specific vesicle population,for example a vesicle with a particular biosignature, and as describedabove. A plurality of probes for detecting prostate specific vesicles isalso provided. A plurality of probes can comprise probes for detectingone or more of the following biomarkers: CD9, PSCA, TNFR, CD63, MFG-E8,EpCAM, Rab, CD81, STEAP, PCSA, 5T4, EpCAM, PSMA, CD59, CD66, CD24 andB7H3. A plurality of probes for detecting Bcl-XL, ERCC1, Keratin 15,CD81/TAPA-1, CD9, Epithelial Specific Antigen (ESA), and Mast CellChymase can also be provided.

A plurality of probes for detecting one or more miRNAs of a vesicle cancomprise probes for detecting one or more of the following miRNAs:miR-9, miR-629, miR-141, miR-671-3p, miR-491, miR-182, miR-125a-3p,miR-324-5p, miR-148b, and miR-222. In another embodiment, the pluralityof probes comprises one or more probes for detecting EpCam, CD9, PCSA,CD63, CD81, PSMA, B7H3, PSCA, ICAM, STEAP, and EGFR. In someembodiments, the plurality of probes comprises one or more probes fordetecting EpCam, CD9, PCSA, CD63, CD81, PSMA, and B7H3. In otherembodiments, the plurality of probes comprises one or more probes fordetecting EpCam, CD9, PCSA, CD63, CD81, PSMA, B7H3, PSCA, ICAM, STEAP,and EGFR. In yet another embodiment, a subset of the plurality of probesare capture agents for one or more of EpCam, CD9, PCSA, CD63, CD81,PSMA, B7H3, PSCA, ICAM, STEAP, and EGFR, and another subset are probesfor detecting one or more of CD9, CD63, and CD81. A plurality of probescan also comprises one or more probes for detecting r miR-92a-2*,miR-147, miR-574-5p, or a combination thereof. A plurality of probes canalso comprise one or more probes for detecting miR-548c-5p, miR-362-3p,miR-422a, miR-597, miR-429, miR-200a, miR-200b or a combination thereof.A plurality of probes can also comprise one or more probes for detectingEpCam, CK, and CD45. In some embodiments, the one or more probes may becapture agents. In another embodiment, the probes may be detectionagents. In yet another embodiment, the plurality of probes comprisescapture and detection agents.

The probes, such as capture agents, may be attached to a solidsubstrate, such as an array or bead. Alternatively, the probes, such asdetection agents, are not attached. The detection system may be an arraybased system, a sequencing system, a PCR-based system, or a bead-basedsystem, such as described above. The detection system can also be amicrofluidic device as described above.

The detection system may be part of a kit. Alternatively, the kit maycomprise the one or more probe sets or plurality of probes, as describedherein. The kit may comprise probes for detecting a vesicle or aplurality of vesicles, such as vesicles in a heterogeneous population.The kit may comprise probes for detecting a homogeneous population ofvesicles. For example, the kit may comprise probes for detecting apopulation of specific cell-of-origin vesicles, or vesicles with thesame specific biosignature.

Portfolios

Portfolios of multiplexed markers to guide clinical decisions anddisease detection and management can be established such that thecombination of biosignatures in the portfolio exhibit improvedsensitivity and specificity relative to individual biosignatures orrandomly selected combinations of biosignatures. In the context of theinstant invention, the sensitivity of the portfolio can be reflected inthe fold differences exhibited by a biosignature's expression in thediseased state relative to the normal state. Specificity can bereflected in statistical measurements of the correlation of thesignaling of gene expression, for example, with the condition ofinterest (e.g. standard deviation can be a used as such a measurement).In considering a group of biosignature for inclusion in a portfolio, asmall standard deviation in measurements correlates with greaterspecificity. Other measurements of variation such as correlationcoefficients can also be used in this capacity.

When combining biomarkers or biosignatures in this invention In VitroDiagnostic Multivariate Index Assays (IVDMIAs) guidelines andregulations may apply. IVDMIAs can apply to biosignatures as defined asa set of 2 or more markers composed of any combination of genes, genealterations, mutations, amplifications, deletions, polymorphisms ormethylations, or proteins, peptides, polypeptides or RNA molecules,miRNAs, mRNAs, snoRNAs, hnRNAs or RNA that can be grouped so thatinformation obtained about the set of biosignatures in the groupprovides a sound basis for making a clinically relevant judgment such asa diagnosis, prognosis, or treatment choice. These sets of biosignaturesmake up various portfolios of the invention. As with most diagnosticmarkers, it is often desirable to use the fewest number of markerssufficient to make a correct medical judgment. This prevents a delay intreatment pending further analysis as well inappropriate use of time andresources. Preferably, portfolios are established such that thecombination of biosignatures in the portfolio exhibit improvedsensitivity and specificity relative to individual biosignatures orrandomly selected combinations of biosignatures. In the context of theinstant invention, the sensitivity of the portfolio can be reflected inthe fold differences exhibited by a biosignature's expression in thediseased state relative to the normal state. Specificity can bereflected in statistical measurements of the correlation of thesignaling of gene expression, for example, with the condition ofinterest. In considering a group of markers in a biosignature forinclusion in a portfolio, standard deviations, variances, co-variances,correlation coefficients, weighted averages, arithmetic sums, means,multiplicative values, weighted or balanced values or any mathematicalmanipulation of the values of 2 or more markers that can together beused to calculate a value or score that taken as a whole can be shown toproduce greater sensitivity, specificity, negative predictive value,positive predictive value or accuracy can also be used in this capacityand are within the scope of this invention.

In another embodiment pattern recognition methods can be used. Oneexample involves comparing biomarker expression profiles for variousbiomarkers (or biosignature portfolios) to ascribe diagnoses. Theexpression profiles of each of the biomarker comprising the biosignatureportfolio are fixed in a medium such as a computer readable medium.

In one example, a table can be established into which the range ofsignals (e.g., intensity measurements) indicative of disease orphysiological state is input. Actual patient data can then be comparedto the values in the table to determine whether the patient samples arenormal, benign, diseased, or represent a specific physiological state.In a more sophisticated embodiment, patterns of the expression signals(e.g., fluorescent intensity) are recorded digitally or graphically. Inthe example of RNA expression patterns from the biomarker portfoliosused in conjunction with patient samples are then compared to theexpression patterns. Pattern comparison software can then be used todetermine whether the patient samples have a pattern indicative of thedisease, a given prognosis, a pattern that indicates likeliness torespond to therapy, or a pattern that is indicative of a particularphysiological state. The expression profiles of the samples are thencompared to the portfolio of a control cell. If the sample expressionpatterns are consistent with the expression pattern(s) for disease,prognosis, or therapy-related response then (in the absence ofcountervailing medical considerations) the patient is diagnosed asmeeting the conditions that relate to these various circumstances. Ifthe sample expression patterns are consistent with the expressionpattern derived from the normal/control vesicle population then thepatient is diagnosed negative for these conditions.

In another exemplary embodiment, a method for establishing biomarkerexpression portfolios is through the use of optimization algorithms suchas the mean variance algorithm widely used in establishing stockportfolios. This method is described in detail in the U.S. ApplicationPublication No. 20030194734, incorporated herein by reference.Alternatively, measured DNA alterations, changes in mRNA, protein, ormetabolites to phenotypic readouts of efficacy and toxicity may bemodeled and analyzed using algorithms, systems and methods described inU.S. Pat. Nos. 7,089,168, 7,415,359 and U.S. Application PublicationNos. 20080208784, 20040243354, or 20040088116, each of which is hereinincorporated by reference in its entirety.

An exemplary process of biosignature portfolio selection andcharacterization of an unknown is summarized as follows:

(1) Choose baseline class.

(2) Calculate mean, and standard deviation of each biomarker forbaseline class samples.

(3) Calculate (X*Standard Deviation+Mean) for each biomarker. This isthe baseline reading from which all other samples will be compared. X isa stringency variable with higher values of X being more stringent thanlower.

(4) Calculate ratio between each Experimental sample versus baselinereading calculated in step 3.

(5) Transform ratios such that ratios less than 1 are negative (eg.using Log base 10). (Under expressed biomarkers now correctly havenegative values necessary for MV optimization).

(6) These transformed ratios are used as inputs in place of the assetreturns that are normally used in the software application.

(7) The software will plot the efficient frontier and return anoptimized portfolio at any point along the efficient frontier.

(8) Choose a desired return or variance on the efficient frontier.

(9) Calculate the Portfolio's Value for each sample by summing themultiples of each gene's intensity value by the weight generated by theportfolio selection algorithm.

(10) Calculate a boundary value by adding the mean BiosignaturePortfolio Value for Baseline groups to the multiple of Y and theStandard Deviation of the Baseline's Biosignature Portfolio Values.Values greater than this boundary value shall be classified as theExperimental Class.

(11) Optionally one can reiterate this process until best prediction.

The process of selecting a biosignature portfolio can also include theapplication of heuristic rules. Preferably, such rules are formulatedbased on biology and an understanding of the technology used to produceclinical results. More preferably, they are applied to output from theoptimization method. For example, the mean variance method ofbiosignature portfolio selection can be applied to microarray data for anumber of biomarkers differentially expressed in subjects with aspecific disease. Output from the method would be an optimized set ofbiomarkers that could include those that are expressed in vesicles aswell as in diseased tissue. If samples used in the testing method areobtained from vesicles and certain biomarkers differentially expressedin instances of disease or physiological state could also bedifferentially expressed in vesicles, then a heuristic rule can beapplied in which a biosignature portfolio is selected from the efficientfrontier excluding those that are differentially expressed in vesicles.Of course, the rule can be applied prior to the formation of theefficient frontier by, for example, applying the rule during datapre-selection.

Other statistical, mathematical and computational algorithms for theanalysis of linear and non-linear feature subspaces, feature extractionand signal deconvolution in large scale datasets to identifyvesicle-derived multiplex analyte profiles for diagnosis, prognosis andtherapy selection and/or characterization of define physiological statescan be done using any combination of unsupervised analysis methods,including but not limited to: principal component analysis (PCA) andlinear and non-linear independent component analysis (ICA); blind sourceseparation, nongaussinity analysis, natural gradient maximum likelihoodestimation; joint-approximate diagonalization; eigenmatrices; Gaussianradical basis function, kernel and polynominal kernel analysissequential floating forward selection.

Computer Systems

A vesicle can be assayed for molecular features, for example, bydetermining an amount, presence or absence of one or more biomarkerssuch as listed FIGS. 1, 3-60. The data generated can be used to producea biosignature, which can be stored and analyzed by a computer system,such as shown in FIG. 65. The assaying or correlating of thebiosignature with one or more phenotypes can also be performed bycomputer systems, such as by using computer executable logic.

A computer system, such as shown in FIG. 65, can be used to transmitdata and results following analysis. Accordingly, FIG. 65 is a blockdiagram showing a representative example logic device through whichresults from a vesicle can be analyzed and the analysis reported orgenerated. FIG. 65 shows a computer system (or digital device) 800 toreceive and store data generated from a vesicle, analyze of the data togenerate one or more biosignatures, and produce a report of the one ormore biosignatures or phenotype characterization. The computer systemcan also perform comparisons and analyses of biosignatures generated,and transmit the results. Alternatively, the computer system can receiveraw data of vesicle analysis, such as through transmission of the dataover a network, and perform the analysis.

The computer system 800 may be understood as a logical apparatus thatcan read instructions from media 811 and/or network port 805, which canoptionally be connected to server 809 having fixed media 812. The systemshown in FIG. 65 includes CPU 801, disk drives 803, optional inputdevices such as keyboard 815 and/or mouse 816 and optional monitor 807.Data communication can be achieved through the indicated communicationmedium to a server 809 at a local or a remote location. Thecommunication medium can include any means of transmitting and/orreceiving data. For example, the communication medium can be a networkconnection, a wireless connection or an interne connection. Such aconnection can provide for communication over the World Wide Web. It isenvisioned that data relating to the present invention can betransmitted over such networks or connections for reception and/orreview by a party 822. The receiving party 822 can be but is not limitedto an individual, a health care provider or a health care manager. Thus,the information and data on a test result can be produced anywhere inthe world and transmitted to a different location. For example, when anassay is conducted in a differing building, city, state, country,continent or offshore, the information and data on a test result may begenerated and cast in a transmittable form as described above. The testresult in a transmittable form thus can be imported into the U.S. toreceiving party 822. Accordingly, the present invention also encompassesa method for producing a transmittable form of information on thediagnosis of one or more samples from an individual. The methodcomprises the steps of (1) determining a diagnosis, prognosis,theranosis or the like from the samples according to methods of theinvention; and (2) embodying the result of the determining step into atransmittable form. The transmittable form is the product of theproduction method. In one embodiment, a computer-readable mediumincludes a medium suitable for transmission of a result of an analysisof a biological sample, such as biosignatures. The medium can include aresult regarding a vesicle, such as a biosignature of a subject, whereinsuch a result is derived using the methods described herein.

Ex Vivo Harvesting of Vesicles

A vesicle for analysis and determination of a phenotype can also be fromex vivo harvesting. Cells can be cultured and vesicles released fromcells of interest in culture either result spontaneously or can bestimulated to release vesicles into the medium. (see for example,Zitvogel, et al. 1998. Nat. Med. 4: 594-600; Chaput, et al. 2004. J.Immunol. 172: 2137-214631: 2892-2900; Escudier, et al. 2005. J. Transl.Med. 3: 10; Morse, et al. 2005, J. Transl. Med. 3: 9; Peche, et al.2006. Am. J. Transplant. 6: 1541-1550; Kim, et al. 2005. J. Immunol.174: 6440-6448, all of which are herein incorporated by reference intheir entireties). Cell lines or tissue samples can be grown to 80%confluence before being cultured in fresh DMEM for 72 h. Subsequentvesicle production can be stimulated (see, for example, heat shocktreatment of melanoma cells as described by Dressel, et al. 2003. CancerRes. 63: 8212-8220, which is herein incorporated by reference in itsentirety). The supernatant can then be harvested and vesicles preparedas described herein.

A vesicle produced ex vivo can, in one example, be cultured from acell-of-origin or cell line of interest, vesicles can be isolated fromthe cell culture medium and subsequently labeled with a magnetic label,a fluorescent moiety, a radioisotope, an enzyme, a chemiluminescentprobe, a metal particle, a non-metal colloidal particle, a polymeric dyeparticle, a pigment molecule, a pigment particle, an electrochemicallyactive species, semiconductor nanocrystal or other nanoparticlesincluding quantum dots or gold particles to be reintroduced in vivo as alabel for imaging analysis. Ex vivo cultured vesicles can alternativelybe used to identify novel biosignatures by setting up culturingconditions for a given cell-of-origin with characteristics of interest,for example a culture of lung cancer cells or cell line with a knownEGFR mutation that confers resistant to or susceptibility to gefitinib,then exposing the cell culture to gefitinib, isolating vesicles thatarise from the culture and subsequently analyzing them on a discoveryarray to look for novel antigens or binding agents expressed on theoutside of vesicles that could be used as a biosignature to capture thisspecies of vesicle. Additionally, it would be possible to isolate anyother biomarkers or biosignatures found within these vesicles fordiscovery of novel signatures (including but not limited to nucleicacids, proteins, lipids, or combinations thereof) that may have clinicaldiagnostic, prognostic or therapy related implication.

Cells of interest can also be first isolated and cultured from tissuesof interest. For example, human hair follicles in the growing phase,anagen, can be plucked individually from a patient's scalp using sterileequipment and plasticware, taking care not to damage the follicle. Eachsample can be transferred to a Petri dish containing sterile PBS fortissue culture. Isolated human anagen hair follicles can be carefullytransferred to an individual well of a 24-well plate containing 1 ml ofWilliam's E medium. Follicles can be maintained free-floating at 37° C.in an atmosphere of 5% CO₂ and 95% air in a humidified incubator. Mediumcan be changed every 3 days, taking care not to damage the follicles.Cells can then be collected and spun down from the media. Vesicles maythen be isolated using antigens or cellular binding partners that arespecific to such cell-of-origin specific vesicles using methods aspreviously described. Biomarkers and biosignatures can then be isolatedand characterized by methods known to those skilled in the art.

Cells of interest may also be cultured under microgravity orzero-gravity conditions or under a free-fall environment. For example,NASA's bioreactor technology will allow such cells to be grown at muchfaster rate and in much greater quantities. Vesicles may then beisolated using antigens or cellular binding partners that are specificto such cell-of-origin specific vesicles using methods as previouslydescribed.

Rotating wall vessels or RWVersus are a class of bioreactors developedby and for NASA that are designed to grow suspension cultures of cellsin a quiescent environment that simulates microgravity can also be used.(see for example, U.S. Pat. Nos. 5,026,650; 5,153,131; 5,153,133;5,437,998; 5,665,594; 5,702,941; 7,351,584, 5,523,228, 5,104,802,6,117,674, Schwarz, R P, et al., J. Tiss. Cult. Meth. 14:51-58, 1992;Martin et al., Trends in biotechnology 2004; 22; 80-86, Li et al.,Biochemical Engineering Journal 2004; 18; 97-104, Ashammakhi et al.,Journal Nanoscience Nanotechnology 2006; 9-10: 2693-2711, Zhang et al.,International Journal of Medicine 2007; 4: 623-638, Cowger, N L, et al.,Biotechnol. Bioeng 64:14-26, 1999, Spaulding, G F, et al., J. Cell.Biochem. 51:249-251, 1993, Goodwin, T J et al., Proc. Soc. Exp. Biol.Med. 202:181-192, 1993; Freed, L E et al., In Vitro Cell. Dev. Biol.33:381-385, 1997, Clejan, S. et al, Biotechnol. Bioeng. 50:587-597,1996). Khaoustov, V I, et al., In Vitro Cell. Dev. Biol. 35:501-509.1999, each of which is herein incorporated by reference in itsentirety).

Alternatively, cells of interest or cell-of-origin specific vesiclesthat have been isolated may be cultured in a stationary phase plug-flowbioreactor as generally described in U.S. Pat. No. 6,911,201, and U.S.Application Publication Nos. 20050181504, 20050180958, 20050176143 and20050176137, each of which is herein incorporated by reference in itsentirety. Alternatively, cells of interest or cell-origin specificvesicles may also be isolated and cultured as generally described inU.S. Pat. No. 5,486,359.

One embodiment can include the steps of providing a tissue specimencontaining cells of interest or cell-origin specific vesicles, addingcells or vesicles from the tissue specimen to a medium which allows,when cultured, for the selective adherence of only the cells of interestor cell-origin specific vesicles to a substrate surface, culturing thespecimen-medium mixture, and removing the non-adherent matter from thesubstrate surface is generally described in U.S. Pat. No. 5,486,359,which is herein incorporated by reference in its entirety.

EXAMPLES Example 1 Purification of Vesicles from Prostate Cancer CellLines

Prostate cancer cell lines are cultured for 3-4 days in culture mediacontaining 20% FBS (fetal bovine serum) and 1% P/S/G. The cells are thenpre-spun for 10 minutes at 400×g at 4° C. The supernatant is kept andcentrifuged for 20 minutes at 2000×g at 4. The supernatant containingvesicles can be concentrated using a Millipore Centricon Plus-70 (Cat #UFC710008 Fisher).

The Centricon is pre washed with 30 mls of PBS at 1000×g for 3 minutesat room temperature. Next, 15-70 mls of the pre-spun cell culturesupernatant is poured into the Concentrate Cup and is centrifuged in aSwing Bucket Adapter (Fisher Cat #75-008-144) for 30 minutes at 1000×gat room temperature.

The flow through in the Collection Cup is poured off. The volume in theConcentrate Cup is brought back up to 60 mls with any additionalsupernatant. The Concentrate Cup is centrifuged for 30 minutes at 1000×gat room temperature to concentrate the cell supernatant.

The Concentrate Cup is washed by adding 70 mls of PBS and centrifugedfor 30-60 minutes at 1000×g until approximately 2 mls remains. Thevesicles are removed from the filter by inverting the concentrate intothe small sample cup and centrifuge for 1 minute at 4° C. The volume isbrought up to 25 mls with PBS. The vesicles are now concentrated and areadded to a 30% Sucrose Cushion.

To make a cushion, 4 mls of Tris/30% Sucrose/D2O solution (30gprotease-free sucrose, 2.4 g Tris base, 50 ml D2O, adjust pH to 7.4 with10N NCL drops, adjust volume to 100 mls with D2O, sterilize by passingthru a 0.22-um filter) is loaded to the bottom of a 30 ml V bottom thinwalled Ultracentrifuge tube. The diluted 25 mls of concentrated vesiclesis gently added above the sucrose cushion without disturbing theinterface and is centrifuged for 75 minutes at 100,000×g at 4° C. The˜25 mls above the sucrose cushion is carefully removed with a 10 mlpipet and the ˜3.5 mls of vesicles is collected with a fine tip transferpipet (SAMCO 233) and transferred to a fresh ultracentrifuge tube, where30 mls PBS is added. The tube is centrifuged for 70 minutes at 100,000×gat 4° C. The supernatant is poured off carefully. The pellet isresuspended in 200 ul PBS and can be stored at 4° C. or used for assays.A BCA assay (1:2) can be used to determine protein content and Westernblotting or electron micrography can be used to determine vesiclepurification.

Example 2 Purification of Vesicles from VCaP and 22Rv1

Vesicles from Vertebral-Cancer of the Prostate (VCaP) and 22Rv1, a humanprostate carcinoma cell line, derived from a human prostatic carcinomaxenograft (CWR22R) were collected by ultracentrifugation by firstdiluting plasma with an equal volume of PBS (1 ml). The diluted fluidwas transferred to a 15 ml falcon tube and centrifuged 30 minutes at2000×g 4° C. The supernatant (˜2 mls) was transferred to anultracentrifuge tube 5.0 ml PA thinwall tube (Sorvall #03127) andcentrifuged at 12,000×g, 4° C. for 45 minutes.

The supernatant (˜2 mls) was transferred to a new 5.0 ml ultracentrifugetubes and filled to maximum volume with addition of 2.5 mls PBS andcentrifuged for 90 minutes at 110,000×g, 4° C. The supernatant waspoured off without disturbing the pellet and the pellet resuspended with1 ml PBS. The tube was filled to maximum volume with addition of 4.5 mlof PBS and centrifuged at 110,000×g, 4° C. for 70 minutes.

The supernatant was poured off without disturbing the pellet and anadditional 1 ml of PBS was added to wash the pellet. The volume wasincreased to maximum volume with the addition of 4.5 mls of PBS andcentrifuged at 110,000×g for 70 minutes at 4° C. The supernatant wasremoved with P-1000 pipette until ˜100 μl of PBS was in the bottom ofthe tube. The ˜90 μl remaining was removed with P-200 pipette and thepellet collected with the ˜10 μl of PBS remaining by gently pipettingusing a P-20 pipette into the microcentrifuge tube. The residual pelletwas washed from the bottom of a dry tube with an additional 5 μl offresh PBS and collected into microcentrifuge tube and suspended inphosphate buffered saline (PBS) to a concentration of 500 μg/ml.

Example 3 Plasma Collection and Vesicle Purification

Blood is collected via standard veinpuncture in a 7 ml K2-EDTA tube. Thesample is spun at 400g for 10 minutes in a 4° C. centrifuge to separateplasma from blood cells (SORVALL Legend RT+ centrifuge). The supernatant(plasma) is transferred by careful pipetting to 15 ml Falcon centrifugetubes. The plasma is spun at 2,000g for 20 minutes and the supernatantis collected.

For storage, approximately 1 ml of the plasma (supernatant) is aliquotedto a cryovials, placed in dry ice to freeze them and stored in −80° C.Before vesicle purification, if samples were stored at −80° C., samplesare thawed in a cold water bath for 5 minutes. The samples are mixed endover end by hand to dissipate insoluble material.

In a first prespin, the plasma is diluted with an equal volume of PBS(example, approximately 2 ml of plasma is diluted with 2 ml of PBS). Thediluted fluid is transferred to a 15 ml Falcon tube and centrifuged for30 minutes at 2000×g at 4° C.

For a second prespin, the supernatant (approximately 4 mls) is carefullytransferred to a 50 ml Falcon tube and centrifuged at 12,000×g at 4° C.for 45 minutes in a Sorval.

In the isolation step, the supernatant (approximately 2 mls) iscarefully transferred to a 5.0 ml ultracentrifuge PA thinwall tube(Sorvall #03127) using a P1000 pipette and filled to maximum volume withan additional 0.5 mls of PBS. The tube is centrifuged for 90 minutes at110,000×g at 4° C.

In the first wash, the supernatant is poured off without disturbing thepellet. The pellet is resuspended or washed with 1 ml PBS and the tubeis filled to maximum volume with an additional 4.5 ml of PBS. The tubeis centrifuged at 110,000×g at 4° C. for 70 minutes. A second wash isperformed by repeating the same steps.

The vesicles are collected by removing the supernatant with P-1000pipette until approximately 100 μl of PBS is in the bottom of the tube.Approximately 90 μl 1 of the PBS is removed and discarded with P-200pipette. The pellet and remaining PBS is collected by gentle pipettingusing a P-20 pipette. The residual pellet is washed from the bottom ofthe dry tube with an additional 5 μl of fresh PBS and collected into amicrocentrifuge tube.

Example 4 Analysis of Vesicles Using Antibody-Coupled Microspheres andDirectly Conjugated Antibodies

This example demonstrates the use of particles coupled to an antibody,where the antibody captures the vesicles. See, e.g., FIG. 63A. Anantibody, the detector antibody, is directly coupled to a label, and isused to detect a biomarker on the captured vesicle.

First, an antibody-coupled microsphere set is selected (Luminex, Austin,Tex.). The microsphere set can comprise various antibodies, and thusallows multiplexing. The microspheres are resuspended by vortex andsonication for approximately 20 seconds. A Working Microsphere Mixtureis prepared by diluting the coupled microsphere stocks to a finalconcentration of 100 microspheres of each set/4 in Startblock (Pierce(37538)). 50 μL of Working Microsphere Mixture is used for each well.Either PBS-1% BSA or PBS-BN (PBS, 1% BSA, 0.05% Azide, pH 7.4) may beused as Assay Buffer.

A 1.2 μm Millipore filter plate is pre-wet with 100 μl/well of PBS-1%BSA (Sigma (P3688-10PAK+0.05% NaAzide (S8032))) and aspirated by vacuummanifold. An aliquot of 50 μl of the Working Microsphere Mixture isdispensed into the appropriate wells of the filter plate (MilliporeMultiscreen HTS (MSBVN1250)). A 50 μl aliquot of standard or sample isdispensed into to the appropriate wells. The filter plate is covered andincubated for 60 minutes at room temperature on a plate shaker. Theplate is covered with a sealer, placed on the orbital shaker and set to900 for 15-30 seconds to re-suspend the beads. Following that the speedis set to 550 for the duration of the incubation.

The supernatant is aspirated by vacuum manifold (less than 5 inches Hgin all aspiration steps). Each well is washed twice with 100 μl ofPBS-1% BSA (Sigma (P3688-10PAK+0.05% NaAzide (S8032))) and is aspiratedby vacuum manifold. The microspheres are resuspended in 50 μL of PBS-1%BSA (Sigma (P3688-10PAK+0.05% NaAzide (S8032))). The PE conjugateddetection antibody is diluted to 4 μg/mL (or appropriate concentration)in PBS-1% BSA (Sigma (P3688-10PAK+0.05% NaAzide (S8032))). (Note: 50 μLof diluted detection antibody is required for each reaction.) A 50 μlaliquot of the diluted detection antibody is added to each well. Thefilter plate is covered and incubated for 60 minutes at room temperatureon a plate shaker. The filter plate is covered with a sealer, placed onthe orbital shaker and set to 900 for 15-30 seconds to re-suspend thebeads. Following that the speed is set to 550 for the duration of theincubation. The supernatant is aspirated by vacuum manifold. The wellsare washed twice with 100 μl of PBS-1% BSA (Sigma (P3688-10PAK+0.05%NaAzide (S8032))) and aspirated by vacuum manifold. The microspheres areresuspended in 100 μl of PBS-1% BSA (Sigma (P3688-10PAK+0.05% NaAzide(S8032))). The microspheres are analyzed on a Luminex analyzer accordingto the system manual.

FIG. 66 depicts antibody-conjugated microspheres with vesicles boundthereto. Specifically, the figure shows scanning electron micrographs(SEMs) of EpCam conjugated beads that have been incubated with VCaPvesicles. For the graphic shown in FIG. 66A, a glass slide was coatedwith poly-L-lysine and incubated with the bead solution. Afterattachment, the beads were (i) fixed sequentially with glutaraldehydeand osmium tetroxide, 30 min per fix step with a few washes in between;(ii) gradually dehydrated in acetone, 20% increments, about 5-7 min perstep; (iii) critical-point dried; and (iv) sputter-coated with gold.FIG. 66B, left depicts a higher magnification of vesicles on an EpCamcoated bead as in FIG. 66A. FIG. 66B, right depicts vesicles isolated byultracentrifugation and adhered to a poly-L-lysine coated glass slideand fixed and stained as in FIG. 66A.

Example 5 Analysis of Vesicles Using Antibody-Coupled Microspheres andBiotinylated Antibody

This example demonstrates the use of particles coupled to an antibody,where the antibody captures the vesicles. An antibody, the detectorantibody, is biotinylated. A label coupled to streptavidin is used todetect the biomarker.

First, the appropriate antibody-coupled microsphere set is selected(Luminex, Austin, Tex.). The microspheres are resuspended by vortex andsonication for approximately 20 seconds. A Working Microsphere Mixtureis prepared by diluting the coupled microsphere stocks to a finalconcentration of 50 microspheres of each set/4 in Startblock (Pierce(37538)). (Note: 50 μl of Working Microsphere Mixture is required foreach well.) Beads in Start Block should be blocked for 30 minutes and nomore than 1 hour.

A 1.2 μm Millipore filter plate is pre-wet with 100 μl/well of PBS-1%BSA+Azide (PBS-BN) ((Sigma (P3688-10PAK+0.05% NaAzide (S8032))) and isaspirated by vacuum manifold. A 50 μl aliquot of the Working MicrosphereMixture is dispensed into the appropriate wells of the filter plate(Millipore Multiscreen HTS (MSBVN1250)). A 50 μl aliquot of standard orsample is dispensed to the appropriate wells. The filter plate iscovered with a seal and is incubated for 60 minutes at room temperatureon a plate shaker. The covered filter plate is placed on the orbitalshaker and set to 900 for 15-30 seconds to re-suspend the beads.Following that, the speed is set to 550 for the duration of theincubation.

The supernatant is aspirated by a vacuum manifold (less than 5 inches Hgin all aspiration steps). Aspiration can be done with the Pall vacuummanifold. The valve is place in the full off position when the plate isplaced on the manifold. To aspirate slowly, the valve is opened to drawthe fluid from the wells, which takes approximately 3 seconds for the100 μl of sample and beads to be fully aspirated from the well. Once thesample drains, the purge button on the manifold is pressed to releaseresidual vacuum pressure from the plate.

Each well is washed twice with 100 μl of PBS-1% BSA+Azide (PBS-BN)(Sigma (P3688-10PAK+0.05% NaAzide (S8032))) and is aspirates by vacuummanifold. The microspheres are resuspended in 50 μl of PBS-1% BSA+Azide(PBS-BN) ((Sigma (P3688-10PAK+0.05% NaAzide (S8032)))

The biotinylated detection antibody is diluted to 4 μg/mL in PBS-1%BSA+Azide (PBS-BN) ((Sigma (P3688-10PAK+0.05% NaAzide (S8032))). (Note:50 μl of diluted detection antibody is required for each reaction.) A 50μl aliquot of the diluted detection antibody is added to each well.

The filter plate is covered with a sealer and is incubated for 60minutes at room temperature on a plate shaker. The plate is placed onthe orbital shaker and set to 900 for 15-30 seconds to re-suspend thebeads. Following that, the speed is set to 550 for the duration of theincubation.

The supernatant is aspirated by vacuum manifold. Aspiration can be donewith the Pall vacuum manifold. The valve is place in the full offposition when the plate is placed on the manifold. To aspirate slowly,the valve is opened to draw the fluid from the wells, which takesapproximately 3 seconds for the 100 ul of sample and beads to be fullyaspirated from the well. Once all of the sample is drained, the purgebutton on the manifold is pressed to release residual vacuum pressurefrom the plate.

Each well is washed twice with 100 μl of PBS-1% BSA+Azide (PBS-BN)((Sigma (P3688-10PAK+0.05% NaAzide (S8032))) and is aspirated by vacuummanifold. The microspheres are resuspended in 50 μl of PBS-1% BSA (Sigma(P3688-10PAK+0.05% NaAzide (S8032))).

The streptavidin-R-phycoerythrin reporter (Molecular Probes 1 mg/ml) isdiluted to 4 μg/mL in PBS-1% BSA+Azide (PBS-BN). 50 μl of dilutedstreptavidin-R-phycoerythrin was used for each reaction. A 50 μl aliquotof the diluted streptavidin-R-phycoerythrin is added to each well.

The filter plate is covered with a sealer and is incubated for 60minutes at room temperature on a plate shaker. The plate is placed onthe orbital shaker and set to 900 for 15-30 seconds to re-suspend thebeads. Following that, the speed is set to 550 for the duration of theincubation.

The supernatant is aspirated by vacuum manifold. Aspiration can be donewith the Pall vacuum manifold. The valve is place in the full offposition when the plate is placed on the manifold. To aspirate slowly,the valve is opened to draw the fluid from the wells, which takesapproximately 3 seconds for the 100 ul of sample and beads to be fullyaspirated from the well. Once all of the sample is drained, the purgebutton on the manifold is pressed to release residual vacuum pressurefrom the plate.

Each well is washed twice with 100 μl of PBS-1% BSA+Azide (PBS-BN)((Sigma (P3688-10PAK+0.05% NaAzide (S8032))) and is aspirated by vacuummanifold. The microspheres are resuspended in 100 μl of PBS-1% BSA+Azide(PBS-BN) ((Sigma (P3688-10PAK+0.05% NaAzide (S8032))) and analyzed onthe Luminex analyzer according to the system manual.

Example 6 Antibody Purification and Carbodiimide Coupling toCarboxylated Microspheres

Antibody Purification Protocol: Antibodies were purified using Protein Gresin from Pierce (Protein G spin kit, Product #89979, Pierce, a part ofThermo Scientific, Rockford, Ill.). Micro-chromatography columns madefrom filtered P-200 tips were used for purification.

One hundred μl of Protein G resin is loaded with 100 μl buffer from thePierce kit to each micro column. After waiting a few minutes to allowthe resin to settle down, air pressure is applied with a P-200 Pipettmanto drain buffer when needed, ensuring the column is not let to dry. Thecolumn is equilibrated with 0.6 ml of Binding Buffer (pH 7.4, 100 mMPhosphate Buffer, 150 mM NaCl; (Pierce, Prod #89979). An antibody isapplied to the column (<1 mg of antibody is loaded on the column). Thecolumn is washed with 1.5 ml of Binding Buffer. Five tubes (1.5 ml microcentrifuge tubes) are prepared and 10 μl of neutralization solution(Pierce, Prod #89979) is applied to each tube. The antibody is elutedwith the elution buffer from the kit to each of the five tubes, 100 ulfor each tube (for a total of 500 μl). The relative absorbance of eachfraction is measured at 280 nm using Nanodrop (Nanodrop 1000spectrophotometer, Nanodrop, a part of Thermo Scientific, Wilmington,Del.). The fractions with highest OD reading are selected for downstreamusage. The samples are dialyzed against 0.25 liters PBS buffer usingPierce Slide-A-Lyzer Dialysis Cassette (Pierce, Product No. 66333, 3 KDacut off). The buffer is exchanged every 2 hours for minimum threeexchanges at 4° C. with continuous stirring. The dialyzed samples arethen transferred to 1.5 ml microcentifuge tubes, and can be labeled andstored at 4° C. (short term) or −20° C. (long term).

Coupling:

Microspheres are coated with their respective antibodies as listed aboveaccording to the following protocol.

The microspheres are protected from prolonged exposure to lightthroughout this procedure. The stock uncoupled microspheres areresuspended according to the instructions described in the ProductInformation Sheet provided with the microspheres (xMAP technologies,MicroPlex™ Microspheres, Luminex Corporation, Austin, Tex.). Five ×106of the stock microspheres are transferred to a USA Scientific 1.5 mlmicrocentrifuge tube (USA Scientific, Inc., Orlando, Fla.). The stockmicrospheres are pelleted by microcentrifugation at ≧8000×g for 1-2minutes at room temperature. The supernatant is removed and the pelletedmicrospheres are resuspended in 100 μl of dH2O by vortex and sonicationfor approximately 20 seconds. The microspheres are pelleted bymicrocentrifugation at ≧8000×g for 1-2 minutes at room temperature. Thesupernatant is removed and the washed microspheres are resuspended in 80μl of 100 mM Monobasic Sodium Phosphate, pH 6.2 by vortex and sonication(Branson 1510, Branson Ultrasonics Corp., Danbury, Conn.) forapproximately 20 seconds. Ten μl of 50 mg/ml Sulfo-NHS (ThermoScientific, Cat#24500) (diluted in dH2O) is added to the microspheresand is mixed gently by vortex. Ten μl of 50 mg/ml EDC (ThermoScientific, Cat#25952-53-8) (diluted in dH2O) is added to themicrospheres and gently mixed by vortexing. The microspheres areincubated for 20 minutes at room temperature with gentle mixing byvortex at 10 minute intervals. The activated microspheres are pelletedby microcentrifugation at ≧8000×g for 1-2 minutes at room temperature.The supernatant is removed and the microspheres are resuspended in 250μl of 50 mM MES, pH 5.0 (MES, Sigma, Cat# M2933, Sigma-Aldrich, St.Louis, Mo.) by vortex and sonication for approximately 20 seconds. OnlyPBS-1% BSA+Azide (PBS-BN) ((Sigma (P3688-10PAK+0.05% NaAzide (S8032)))should be used as assay buffer as well as wash buffer. The microspheresare then pelleted by microcentrifugation at ≧8000×g for 1-2 minutes atroom temperature.

The supernatant is removed and the microspheres are resuspended in 250μl of 50 mM MES, pH 5.0 (MES, Sigma, Cat# M2933) by vortex andsonication for approximately 20 seconds. Only PBS-1% BSA+Azide (PBS-BN)((Sigma (P3688-10PAK+0.05% NaAzide (S8032))) should be used as assaybuffer as well as wash buffer. The microspheres are then pelleted bymicrocentrifugation at ≧8000×g for 1-2 minutes at room temperature, thuscompleting two washes with 50 mM MES, pH 5.0.

The supernatant is removed and the activated and washed microspheres areresuspended in 100 μl of 50 mM MES, pH 5.0 by vortex and sonication forapproximately 20 seconds. Protien in the amount of 125, 25, 5 or 1 μg isadded to the resuspended microspheres. (Note: Titration in the 1 to 125μg range can be performed to determine the optimal amount of protein perspecific coupling reaction.). The total volume is brought up to 500 μlwith 50 mM MES, pH 5.0. The coupling reaction is mixed by vortex and isincubated for 2 hours with mixing (by rotating on Labquake rotator,Barnstead, Thermo Scientific) at room temperature. The coupledmicrospheres are pelleted by microcentrifugation at ≧8000×g for 1-2minutes at room temperature. The supernatant is removed and the pelletedmicrospheres are resuspended in 5004 of PBS-TBN by vortex and sonicationfor approximately 20 seconds. (Concentrations can be optimized forspecific reagents, assay conditions, level of multiplexing, etc. inuse.).

The microspheres are incubated for 30 minutes with mixing (by rotatingon Labquake rotator, Barnstead) at room temperature. The coupledmicrospheres are pelleted by microcentrifugation at ≧8000×g for 1-2minutes at room temperature. The supernatant is removed and themicrospheres are resuspended in 1 ml of PBS-TBN by vortex and sonicationfor approximately 20 seconds. Each time there is the addition ofsamples, detector antibody or SA-PE the plate is covered with a sealerand light blocker (such as aluminum foil), placed on the orbital shakerand set to 900 for 15-30 seconds to re-suspend the beads. Following thatthe speed should be set to 550 for the duration of the incubation.

The microspheres are pelleted by microcentrifugation at ≧8000×g for 1-2minutes. The supernatant is removed and the microspheres are resuspendedin 1 ml of PBS-TBN by vortex and sonication for approximately 20seconds. The microspheres are pelleted by microcentrifugation at ≧8000×gfor 1-2 minutes (resulting in a total of two washes with 1 ml PBS-TBN).

Example 7 Vesicle Concentration from Plasma

Supplies and Equipment:

Pall life sciences Acrodisc, 25 mm syringe filter w/1.2 um, Versapormembrane (sterile) Part number: 4190; Pierce concentrators 7 ml/150 KMWCO (molecular weight cut off), Part number: 89922; BD syringe filter,10 ml, Part number: 305482; Sorvall Legend RT Plus Series BenchtopCentrifuge w 15 ml swinging bucket rotor; PBS, pH 7.4, Sigmacat#P3813-10PAK prepared in Sterile Molecular grade water; Co-polymer1.7 ml microfuge tubes, USA Scientific, cat#1415-2500. Water used forreagents is Sterile Filtered Molecular grade water (Sigma, cat#W4502).Handling of patient plasma is done in a biosafety hood.

Procedure:

1. Filter procedure for plasma samples

-   -   1.1. Remove plasma samples from −80° C. (−65° C. to −85° C.)        freezer    -   1.2. Thaw samples in room temperature water (10-15 minutes).    -   1.3. Prepare syringe and filter by removing the number necessary        from their casing.    -   1.4. Pull plunger to draw 4 mL of sterile molecular grade water        into the syringe. Attach a 1.2 μm filter to the syringe tip and        pass contents through the filter onto the 7 ml/150 K MWCO Pierce        column.    -   1.5. Cap the columns and place in the swing bucket centrifuge at        spin at 1000×g in Sorvall Legend RT plus centrifuge for 4        minutes at 20° C. (16° C.-24° C.).    -   1.6. While spinning, disassemble the filter from syringe. Then        remove plunger from syringe.    -   1.7. Discard flow through from the tube and gently tap column on        paper towels to remove any residual water.    -   1.8. Measure and record starting volumes for all plasma samples.        Samples with a volume less than 900 μl may not be processed.    -   1.9. Place open syringe and filter on open Pierce column. Fill        open end of syringe with 5.2 mL of 1×PBS and pipette mix plasma        into PBS three to four times.    -   1.10. Replace the plunger of the syringe and slowly depress the        plunger until the contents of the syringe have passed through        the filter onto the Pierce column. Contents should pass through        the filter drop wise.

2. Microvesicle concentration centrifugation protocol

-   -   2.1. Spin 7 ml/150 K MWCO Pierce columns at 2000×g at 20° C.        (16° C.-24° C.) for 60 minutes or until volume is reduced to        250-300 μL. If needed, spin for additional 15 minutes increments        to reach required volume.    -   2.2. At the conclusion of the spin, pipette mix on the column        15× (avoid creating bubbles) and withdraw volume (300 μL or        less) and transfer to a new 1.7 mL co-polymer tube.    -   2.3. The final volume of the plasma concentrate is dependent on        the initial volume of plasma. Plasma is concentrated to 300 ul        if the original plasma volume is 1 ml. If the original volume of        plasma is less than 1 ml, then the volume of concentrate should        be consistent with that ratio. For example, if the original        volume is 900 ul, then the volume of concentrate is 270 ul. The        equation to follow is: x=(y/1000)*300, where x is the final        volume of concentrate and y is the initial volume of plasma.    -   2.4. Record the sample volume and add 1×PBS to the sample to        make the final sample volume.    -   2.5. Store concentrated microvesicle sample at 4° C. (2° C. to        8° C.).

Calculations:

1. Final volume of concentrated plasma sample

-   -   x=(y/1000)*300, where x is the final volume of concentrate and y        is the initial volume of plasma.

Example 8 Determining Bio-Signatures for Prostate Cancer UsingMultiplexing

The samples obtained using methods as described in Example 3 are used inmultiplexing assays as described in Examples 4 and 5. The detectionantibodies used are CD63, CD9, CD81, B7H3 and EpCam. The captureantibodies used are CD9, PSCA, TNFR, CD63 2X, B7H3, MFG-E8, EpCam 2X,CD63, Rab, CD81, SETAP, PCSA, PSMA, 5T4, Rab IgG (control) and IgG(control), resulting in 100 combinations to be screened (FIG. 63C).

Ten prostate cancer patients and 12 normal control patients werescreened. Results for the indicated capture and/or detection antibodiesare depicted in FIG. 68 and FIG. 70A. FIG. 70B depicts the results ofusing PCSA capture antibodies (FIG. 70B, left graph) or EpCam captureantibodies (FIG. 70B, right graph), and detection using one or moredetector antibodies. The sensitivity and specificity of the differentcombinations is depicted in FIG. 73.

Example 9 Determining Bio-Signatures for Colon Cancer Using Multiplexing

Vesicle samples obtained using methods as described in Example 3 areused in multiplexing assays as described in Examples 4 and 5. Thedetection antibodies used are CD63, CD9, CD81, B7H3 and EpCam. Thecapture antibodies used are CD9, PSCA, TNFR, CD63 2X, B7H3, MFG-E8,EpCam 2X, CD63, Rab, CD81, STEAP, PCSA, PSMA, 5T4, Rab IgG (control) andIgG (control), resulting in 100 combinations to be screened.

The results are depicted in FIGS. 69, 71, and 72. The sensitivity of thedifferent combinations is depicted in FIG. 74.

Example 10 Capture of Vesicles Using Magnetic Beads

Vesicles isolated as described in Example 2 are used. Approximately 40ul of the vesicles are incubated with approximately 5 ug (˜50 μl) ofEpCam antibody coated Dynal beads (Invitrogen, Carlsbad, Calif.) and 50μl of Starting Block. The vesicles and beads are incubated with shakingfor 2 hours at 45° C. in a shaking incubator. The tube containing theDynal beads is placed on the magnetic separator for 1 minute and thesupernatant removed. The beads are washed twice and the supernatantremoved each time. Wash beads twice, discarding the supernatant eachtime.

Example 11 Detection of mRNA Transcripts in Vesicles

RNA from the bead-bound vesicles of Example 10 was isolated using theQiagen miRneasy™ kit, (Cat. No. 217061), according to the manufacturer'sinstructions.

The vesicles are homogenized in QIAzol™ Lysis Reagent (Qiagen Cat. No.79306). After addition of chloroform, the homogenate is separated intoaqueous and organic phases by centrifugation. RNA partitions to theupper, aqueous phase, while DNA partitions to the interphase andproteins to the lower, organic phase or the interphase. The upper,aqueous phase is extracted, and ethanol is added to provide appropriatebinding conditions for all RNA molecules from 18 nucleotides (nt)upwards. The sample is then applied to the RNeasy™ Mini spin column,where the total RNA binds to the membrane and phenol and othercontaminants are efficiently washed away. High quality RNA is theneluted in RNase-free water.

RNA from the VCAP bead captured vesicles was measured with the TaqmanTMPRSS:ERG fusion transcript assay (Kirsten D. Mertz et al. Neoplasia.2007 March; 9(3): 200-206.). RNA from the 22Rv1 bead captured vesicleswas measured with the Taqman SPINK1 transcript assay (Scott A. Tomlinset al. Cancer Cell 2008 June 13(6):519-528). The GAPDH transcript(control transcript) was also measured for both sets of vesicle RNA.

Higher CT values indicate lower transcript expression. One change incycle threshold (CT) is equivalent to a 2 fold change, 3 CT differenceto a 4 fold change, and so forth, which can be calculated with thefollowing: 2̂^(CT1-CT2). This experiment shows a difference in CT of theexpression of the fusion transcript TMPRSS:ERG and the equivalentcaptured with the IgG2 negative control bead (FIG. 75). The samecomparison of the SPINK1 transcript in 22RV1 vesicles shows a CTdifference of 6.14 for a fold change of 70.5 (FIG. 75C). Results withGAPDH were similar.

Example 12 Obtaining Serum Samples from Subjects

Blood is collected from subjects (both healthy subjects and subjectswith cancer) in EDTA tubes, citrate tubes or in a 10 ml Vacutainer SSTplus Blood Collection Tube (BD367985 or BD366643, BD Biosciences). Bloodis processed for plasma isolation within 2 h of collection.

Samples are allowed to sit at room temperature for a minimum of 30 minand a max of 2 h. Separation of the clot is accomplished bycentrifugation at 1,000-1,300×g at 4° C. for 15-20 min. The serum isremoved and dispensed in aliquots of 500 μl into 500 to 750 μlcryotubes. Specimens are stored at −80° C.

At a given sitting, the amount of blood drawn can range from ˜20 to ˜90ml. Blood from several EDTA tubes is pooled and transferred toRNase/DNase-free 50-ml conical tubes (Greiner), and centrifuged at1,200×g at room temperature in a Hettich Rotanta 460R benchtopcentrifuge for 10 min. Plasma is transferred to a fresh tube, leavingbehind a fixed height of 0.5 cm plasma supernatant above the pellet toavoid disturbing the pellet. Plasma is aliquoted, with inversion to mixbetween each aliquot, and stored at −80° C.

Example 13 RNA Isolation from Human Plasma and Serum Samples

Four hundred μl of human plasma or serum is thawed on ice and lysed withan equal volume of 2× Denaturing Solution (Ambion). RNA is isolatedusing the mirVana PARIS kit following the manufacturer's protocol forliquid samples (Ambion), modified such that samples are extracted twicewith an equal volume of acid-phenol chloroform (as supplied by theAmbion kit). RNA is eluted with 105 μl of Ambion elution solutionaccording to the manufacturer's protocol. The average volume of eluaterecovered from each column is about 80 μl.

A scaled-up version of the mirVana PARIS (Ambion) protocol is also used:10 ml of plasma is thawed on ice, two 5-ml aliquots are transferred to50-ml tubes, diluted with an equal volume of mirVana PARIS 2× DenaturingSolution, mixed thoroughly by vortexing for 30 s and incubated on icefor 5 min. An equal volume (10 ml) of acid/phenol/chloroform (Ambion) isthen added to each aliquot. The resulting solutions are vortexed for 1min and spun for 5 min at 8,000 rpm, 20° C. in a JA17 rotor. Theacid/phenol/chloroform extraction is repeated three times. The resultingaqueous volume is mixed thoroughly with 1.25 volumes of 100%molecular-grade ethanol and passed through a mirVana PARIS column insequential 700 μl aliquots. The column is washed following themanufacturer's protocol, and RNA is eluted in 105 μl of elution buffer(95° C.). A total of 1.5 μl of the eluate is quantified by Nanodrop.

Example 14 Measurement of miRNA Levels in RNA from Plasma and SerumUsing qRT-PCR

A fixed volume of 1.67 μl of RNA solution from about ˜80 μl-eluate fromRNA isolation of a given sample is used as input into the reversetranscription (RT) reaction. For samples in which RNA is isolated from a400-μl plasma or serum sample, for example, 1.67 μl of RNA solutionrepresents the RNA corresponding to (1.67/80)×400=8.3 μl plasma orserum. For generation of standard curves of chemically synthesized RNAoligonucleotides corresponding to known miRNAs, varying dilutions ofeach oligonucleotide are made in water such that the final input intothe RT reaction has a volume of 1.67 μl. Input RNA is reversetranscribed using the TaqMan miRNA Reverse Transcription Kit andmiRNA-specific stem-loop primers (Applied BioSystems) in a small-scaleRT reaction comprised of 1.387 μl of H2O, 0.5 μl of 10×Reverse-Transcription Buffer, 0.063 μl of RNase-Inhibitor (20 units/μl),0.05 μl of 100 mM dNTPs with dTTP, 0.33 μl of MultiscribeReverse-Transcriptase, and 1.67 μl of input RNA; components other thanthe input RNA can be prepared as a larger volume master mix, using aTetrad2 Peltier Thermal Cycler (BioRad) at 16° C. for 30 min, 42° C. for30 min and 85° C. for 5 min. Real-time PCR is carried out on an AppliedBioSystems 7900HT thermocycler at 95° C. for 10 min, followed by 40cycles of 95° C. for 15 s and 60° C. for 1 min. Data is analyzed withSDS Relative Quantification Software version 2.2.2 (AppliedBioSystems.), with the automatic Ct setting for assigning baseline andthreshold for Ct determination.

The protocol can also be modified to include a preamplification step,such as for detecting miRNA. A 1.25-μl aliquot of undiluted RT productis combined with 3.75 μl of Preamplification PCR reagents [comprised,per reaction, of 2.5 μl of TaqMan PreAmp Master Mix (2×) and 1.25 μl of0.2× TaqMan miRNA Assay (diluted in TE)] to generate a 5.0-μlpreamplification PCR, which is carried out on a Tetrad2 Peltier ThermalCycler (BioRad) by heating to 95° C. for 10 min, followed by 14 cyclesof 95° C. for 15 s and 60° C. for 4 min. The preamplification PCRproduct is diluted (by adding 20 μl of H2O to the 5-μl preamplificationreaction product), following which 2.25 μl of the diluted material isintroduced into the real-time PCR and carried forward as described.

Example 15 Generation of Standard Curves for Absolute Quantification ofmiRNAs

Synthetic single-stranded RNA oligonucleotides corresponding to themature miRNA sequence (miRBase Release v.10.1) are purchased from Sigma.Synthetic miRNAs are input into the RT reaction over anempirically-derived range of copies to generate standard curves for eachof the miRNA TaqMan assays listed above. In general, the lower limit ofaccurate quantification for each assay is designated based on theminimal number of copies input into an RT reaction that results in a Ctvalue within the linear range of the standard curve and that is also notequivalent to or higher than a Ct obtained from an RT input of lowercopy number. A line is fit to data from each dilution series using Ctvalues within the linear range, from which y=mln(x)+b equations arederived for quantification of absolute miRNA copies (x) from each sampleCt (y). Absolute copies of miRNA input into the RT reaction areconverted to copies of miRNA per microliter plasma (or serum) based onthe knowledge that the material input into the RT reaction correspondsto RNA from 2.1% of the total starting volume of plasma [i.e., 1.67 μlof the total RNA eluate volume (80 μl on average) is input into the RTreaction]. An example of a synthetic miRNA sequence is for miR-141 whichcan be obtained commercially such as from Sigma (St. Louis, Mo.).

Example 16 Extracting microRNA from Vesicles

MicroRNA is extracted from vesicles isolated from patient samples asdescribed herein. See, e.g., Examples 7, 49. Methods for isolation andconcentration of vesicles are presented herein. The methods in thisExample can also be used to isolate microRNA from patient sampleswithout first isolating vesicles.

Protocol Using Trizol

This protocol uses the QIAzol Lysis Reagent and RNeasy Midi Kit fromQiagen Inc., Valencia Calif. to extract microRNA from concentratedvesicles. The steps of the method comprise:

1. Add 2 μl of RNase A to 50 μl of vesicle concentrate, incubate at 37°C. for 20 min.2. Add 700 μl of QIAzol Lysis Reagent, vortex 1 minute. Spike sampleswith 25 fmol/μL of C. elegans microRNA (1 μL) after the addition ofQIAzol, making a 75 fmol/μL spike in for each total sample (3 aliquotscombined).

3. Incubate at 55° C. for 5 min.

4. Add 140 μl chloroform and shake vigorously for 15 sec.

5. Cool on ice for 2-3 min. 6. Centrifuge @ 12,000×g at 4° C. for 15min.

7. Transfer aqueous phase (300 μL) to a new tube and add 1.5 volumes of100% EtOH (i.e., 450 μL).8. Pipet up to 4 ml of sample into an RNeasy Midi spin column in a 15 mlcollection tube (combining lysis from 3 50 μl of concentrate)9. Spin at 2700×g for 5 min at room temperature.10. Discard flowthrough from the spin.11. Add 1 ml of Buffer RWT to column and centrifuge at 2700×g for 5 minat room temperature. Do not use Buffer RW1 supplied in the Midi kit.Buffer RW1 can wash away miRNA. Buffer RWT is supplied in the Mini kitfrom Qiagen Inc.12. Discard flowthrough.13. Add 1 ml of Buffer RPE onto the column and centrifuge at 2700×g for2 min at room temperature.14. Repeat steps 12 and 13.16. Place column into a new 15 ml collection tube and add 150 ul ElutionBuffer. Incubate at room temperature for 3 min.17. Centrifuge at 2700×g for 3 min at room temperature.18. Vortex the sample and transfer to 1.7 mL tube. Store the extractedsample at −80° C.

Protocol using MagMax

This protocol uses the MagMAX™ RNA Isolation Kit from AppliedBiosystems/Ambion, Austin, Tex. to extract microRNA from concentratedvesicles. The steps of the method comprise:

1. Add 700 ml of QIAzol Lysis Reagent and vortex 1 minute.2. Incubate on benchtop at room temperature for 5 min.3. Add 140 μl chloroform and shake vigorously for 15 sec.4. Incubate on benchtop for 2-3 min.

5. Centrifuge at 12,000×g at 4° C. for 15 min

6. Transfer aqueous phase to a deep well plate and add 1.25 volumes of100% Isopropanol.7. Shake MagMAX™ binding beads well. Pipet 10 μl of RNA binding beadsinto each well.8. Gather two elution plates and two additional deep well plates.9. Label one elution plate “Elution” and the other “Tip Comb.”10. Label one deep well as “1st Wash 2” and the other as “2nd Wash 2.”11. Fill both Wash 2 deep well plates with 150 μl of Wash 2, being sureto add ethanol to wash beforehand. Fill in the same number of wells asthere are samples.12. Select the appropriate collection program on the MagMax ParticleProcessor.13. Press start and load each appropriate plate.14. Transfer samples to microcentrifuge tubes.15. Vortex and store at −80° C. Residual beads will be seen in sample.

Example 17 MicroRNA Arrays

MicroRNA levels in a sample can be analyzed using an array format,including both high density and low density arrays. Array analysis canbe used to discover differentially expressed in a desired setting, e.g.,by analyzing the expression of a plurality of miRs in two samples andperforming a statistical analysis to determine which ones aredifferentially expressed between the samples and can therefore be usedin a biosignature. The arrays can also be used to identify a presence orlevel of one or more microRNAs in a single sample in order tocharacterize a phenotype by identifying a biosignature in the sample.This Example describes commercially available systems that are used tocarry out the methods of the invention.

TaqMan Low Density Array

TaqMan Low Density Array (TLDA) miRNA cards are used to compareexpression of miRNA in various sample groups as desired. The miRNA arecollected and analyzed using the TaqMan® MicroRNA Assays and Arrayssystems from Applied Biosystems, Foster City, Calif. Applied BiosystemsTaqMan® Human MicroRNA Arrays are used according to the Megaplex™ PoolsQuick Reference Card protocol supplied by the manufacturer.

Exiqon mIRCURY LNA microRNA

The Exiqon miRCURY LNA™ Universal RT microRNA PCR Human Panels I and II(Exiqon, Inc, Woburn, Mass.) are used to compare expression of miRNA invarious sample groups as desired. The Exiqon 384 well panels include 750miRs. Samples are normalized to control primers towards synthetic RNAspike-in from Universal cDNA synthesis kit (UniSp6 CP). Results werenormalized to inter-plate calibrator probes.

With either system, quality control standards are implemented.Normalized values for each probe across three data sets for eachindication are averaged. Probes with an average CV % higher than 20% arenot used for analysis. Results are subjected to a paired t-test to finddifferentially expressed miRs between two sample groups. P-values arecorrected with a Benjamini and Hochberg false-discovery rate test.Results are analyzed using GeneSpring software (Agilent Technologies,Inc., Santa Clara, Calif.).

Example 18 MicroRNA Profiles in Vesicles

Vesicles were collected by ultracentrifugation from 22Rv1, LNCaP, Vcapand normal plasma (pooled from 16 donors) as described in Examples 1-3.RNA was extracted using the Exiqon miR isolation kit (Cat. Nos. 300110,300111). Equals amounts of vesicles (30 μg) were used as determined byBCA assay.

Equal volumes (5 μl) were put into a reverse-transcription reaction formicroRNA. The reverse-transcriptase reactions were diluted in 81 μl ofnuclease-free water and then 9 μl of this solution was added to eachindividual miR assay. MiR-629 was found to only be expressed in PCa(prostate cancer) vesicles and was virtually undetectable in normalplasma vesicles. MiR-9 was found to be highly overexpressed (˜704 foldincrease over normal as measured by copy number) in all PCa cell lines,and has very low expression in normal plasma vesicles. The top tendifferentially expressed miRNAs are shown in FIG. 76.

Example 19 MicroRNA Profiles of Magnetic EpCam-Captured Vesicles

The bead-bound vesicles of Example 10 were placed in QIAzol™ LysisReagent (Qiagen Cat. #79306). An aliquot of 125 fmol of c. elegansmiR-39 was added. The RNA was isolated using the Qiagen miRneasy™ kit,(Cat. #217061), according to the manufacturer's instructions, and elutedin 30 ul RNAse free water.

10 μl the purified RNA was placed into a pre-amplification reaction formiR-9, miR-141 and miR-629 using a Veriti 96-well thermocycler. A 1:5dilution of the pre-amplification solution was used to set up a qRT-PCRreaction for miR9 (ABI 4373285), miR-141 (ABI 4373137) and miR-629 (ABI4380969) as well as c. elegans miR-39 (ABI 4373455). The results werenormalized to the c. elegans results for each sample.

Example 20 MicroRNA Profiles of CD9-Captured Vesicles

CD9 coated Dynal beads (Invitrogen, Carlsbad, Calif.) were used insteadof EpCam coated beads as in Example 19. Vesicles from prostate cancerpatients, LNCaP, or normal purified vesicles were incubated with the CD9coated beads and the RNA isolated as described in Example 19. Theexpression of miR-21 and miR-141 was detected by qRT-PCR and the resultsdepicted in FIGS. 77 and 78.

Example 21 Isolation of Vesicles Using a Filtration Module

Six mL of PBS is added to 1 mL of plasma. The sample is then put througha 1.2 micron (μm) Pall syringe filter directly into a 100 kDa MWCO(Millipore, Billerica, Mass.), 7 ml column with a 150 kDa MWCO (Pierce®,Rockford, Ill.), 15 ml column with a 100 kDa MWCO (Millipore, Billerica,Mass.), or 20 ml column with a 150 kDa MWCO (Pierce®, Rockford, Ill.).

The tube is centrifuged for between 60 to 90 minutes until the volume isabout 250 μl. The retentate is collected and PBC added to bring thesample up to 300 μl. Fifty ul of the sample is then used for furthervesicle analysis, such as further described in the examples below.

Example 22 Multiplex Analysis of Vesicles Isolated with Filters

The vesicle samples obtained using methods as described in Example 21are used in multiplexing assays as described herein. See, e.g., Examples31-33. The capture antibodies are CD9, CD63, CD81, PSMA, PCSA, B7H3, andEpCam. The detection antibodies are for biomarkers CD9, CD81, and CD63or B7H3 and EpCam, as depicted in FIGS. 79, 80, and 81.

Example 23 Vesicle Isolation with Filters from Patient Samples

Vesicle samples obtained using methods as described in Example 21, usinga 7 mL Pierce® concentrator with a 150 kDa MWCO (Cat. #89920/89922) andare used in multiplexing assays as described in herein. See, e.g.,Examples 31-33. The capture antibodies are CD9, CD63, CD81, PSMA, PCSA,B7H3, and EpCam. The detection antibodies used are CD63, CD9, and CD81.The results are shown in FIG. 82.

Example 24 Comparison of Vesicles Isolated with Filters Versus withUltracentrifugation

The vesicle samples obtained using methods as described in Example 21,using a 500 μl column with a 100 kDa MWCO and are used in multiplexingassays as described herein. See, e.g., Examples 31-33. The captureantibodies are CD9, CD63, CD81, PSMA, PCSA, B7H3, and EpCam. Thedetection antibodies are CD63, CD9, and CD81. The results are shown inFIGS. 83 and 84. Each figure shows different analysis methods performedusing samples from a single patient. In both figures, the graphs depictA) ultracentrifugation purified sample; B) filtered sample C)ultracentrifugation purified sample and 10 ug Vcap and D) filteredsample with 10 ug Vcap.

Example 25 Sample Filter Comparison

A variety of filters can be used to remove large debris from the plasmasample as described. Filters ranging in size from 1.2 to 0.8 micronfilters provide comparable results, as shown in Tables 18 and 19:

TABLE 18 Filters tested Pore Vendor Cat No. Membrane Size Pall 4190 1.2uM Millipore SLAA033SB 0.8 uM Millipore SLAA033SS 0.8 uM GVSFJ25ANCCA012CC01 1.2 uM Whatman 6822-1312 GF/C glass fiber (13 mm) 1.2uM Whatman 6750-2510 Nylon 1.0 uM Whatman 6781-2510 PES 1.0 uM Whatman10 462 261 Cellulose Acetate (30 mm) 1.2 uM Whatman 6783-2510 GD glassfiber 1.0 uM

Plasma samples were filtered and vesicles detected using the biomarkersCD9, PSMA, PCSA, CD63, CD81, B7H3 and EpCam, as indicated in Table 19.Methodology was as described herein. See, e.g., Examples 31-33. Sampleswere performed in duplicate. The results for the various filters werecomparable within each marker.

TABLE 19 MFI using various filters to isolate vesicles CD9 PSMA PCSACD63 CD81 B7-H3 EpCam High 28303 23417 22815 28630 28513 24045 27389Blank 23 10 9 216 7 8 18 Pall 4190 726 15 152 1562 2250 172 94 Pall 4190787 18 173 1701 2539 208 100 GVS 631 17 163 1722 2738 182 86 GVS 562 19164 1504 2315 206 95 SLAA033SB 739 26 243 1521 2078 277 135 SLAA033SB725 23 160 1316 1790 263 116 SLAA033SS 888 19 191 1384 2025 223 124SLAA033SS 774 22 173 1392 2063 218 100 6822-1312 824 19 196 1601 2359207 106 6822-1312 697 18 172 1632 2580 202 93 10 462 261 576 19 178 14952420 190 93 10 462 261 553 23 191 1465 2091 221 106 6750-2510 743 32 2481574 2082 286 144 6750-2510 847 38 250 1545 2019 322 171 6783-2510 85319 178 1474 2185 219 110 6783-2510 757 20 159 1533 2232 199 1036781-2510 624 23 202 1433 2189 227 111 6781-2510 711 21 196 1365 1966218 131

Example 26 Flow Cytometry Analysis of Vesicles

Purified plasma vesicles are assayed using the MoFlo XDP (BeckmanCoulter, Fort Collins, Colo., USA) and the median fluorescent intensityanalyzed using the Summit 4.3 Software (Beckman Coulter). Vesicles arelabeled directly with antibodies, or beads or microspheres (e.g.,magnetic, polystyrene, including BD FACS 7-color setup, catalog no.335775) can be incorporated. Vesicles can be detected with bindingagents against the following vesicle antigens: CD9 (Mouse anti-humanCD9, MAB1880, R&D Systems, Minneapolis, Minn., USA), PSM (Mouseanti-human PSM, sc-73651, Santa Cruz, Santa Cruz, Calif., USA), PCSA(Mouse anti-human Prostate Cell Surface Antigen, MAB4089, Millipore,Mass., USA), CD63 (Mouse anti-human CD63, 556019, BD Biosciences, SanJose, Calif., USA), CD81 (Mouse anti-human CD81, 555675, BD Biosciences,San Jose, Calif., USA) B7-H3 (Goat anti-human B7-H3, AF1027, R&DSystems, Minneapolis, Minn., USA), EpCAM (Mouse anti-human EpCAM,MAB9601, R&D Systems, Minneapolis, Minn., USA). Vesicles can be detectedwith fluorescently labeled antibodies against the desired vesicleantigens. For example, FITC, phycoerythrin (PE) and Cy7 are commonlyused to label the antibodies.

To capture the antibodies with multiplex microspheres, the microspherescan be obtained from Luminex (Austin, Tex., USA) and conjugated to thedesired antibodies using micros using Sulfo-NHS and EDC obtained fromPierce Thermo (Cat. No. 24510 and 22981, respectively, Rockford, Ill.,USA).

Purified vesicles (10 ug/ml) are incubated with 5,000 microspheres forone hour at room temperature with shaking. The samples are washed inFACS buffer (0.5% FBS/PBS) for 10 minutes at 1700 rpms. The detectionantibodies are incubated at the manufacturer's recommendedconcentrations for one hour at room temperature with shaking. Followinganother wash with FACS buffer for 10 minutes at 1700 rpms, the samplesare resuspended in 100 ul FACS buffer and run on the FACS machine.

Further when using microspheres to detect vesicles, the labeled vesiclescan be sorted according to their detection antibody content intodifferent tubes. For example, using FITC or PE labeled microspheres, afirst tube contains the population of microspheres with no detectors,the second tube contains the population with PE detectors, the thirdtube contains the population with FITC detectors, and the fourth tubecontains the population with both PE and FITC detectors. The sortedvesicle populations can be further analyzed, e.g., by examining payloadsuch as mRNA, microRNA or protein content.

FIG. 85 shows separation and identification of vesicles using the MoFloXDP. In this set of experiments, there were about 3000 trigger eventswith just buffer (i.e. particulates about the size of a large vesicle).There were about 46,000 trigger events with unstained vesicles (43,000vesicles of sufficient size to scatter the laser). There were 500,000trigger events with stained vesicles. Vesicles were detected usingdetection agents for tetraspanins CD9, CD63, and CD81 all labeled withFITC. The smaller vesicles can be detected when they are stained withdetection agents.

FIG. 86A shows flow sorting of vesicles from the plasma of a prostatecancer patient using Cy7-labeled anti-PSCA antibodies. The percentage ofPCSA+ vesicles increased from 35% to 68% upon flow sorting. FIG. 86Bshows flow sorting of vesicles from the plasma of a normal patient and aprostate cancer patient using labeled anti-CD45 antibodies. There isapproximately a five-fold greater percentage of CD45+ vesicles in thecancer plasma compared with the healthy control. The percentage of CD45+vesicles was greatly increased after flow sorting. As CD45 is an immuneresponse marker, the increased immune derived vesicles demonstrate animmune response in the prostate cancer patient. FIG. 86C shows flowsorting of vesicles from the plasma of a normal patient and a breastcancer patient using labeled anti-CD45 antibodies. There are more than a10-fold greater percentage of CD45+ vesicles in the breast cancer plasmacompared with the healthy control. The percentage of CD45+ vesicles wasgreatly increased after sorting. As CD45 is an immune response marker,the increased immune derived vesicles demonstrate an immune response inthe breast cancer patient. FIG. 86D shows flow sorting of vesicles fromthe plasma of a normal patient and a prostate cancer patient usinglabeled anti-DLL4 antibodies. There are approximately a 10-fold greaterpercentage of DLL4+ vesicles in the prostate cancer plasma compared withthe healthy control. The percentage of DLL4+ vesicles was greatlyincreased by flow sorting. Increased DLL4+ vesicles may indicateincreased angiogenenesis in the cancer patients.

Physical isolation by sorting of specific populations of vesiclesfacilitates additional studies such as microRNA analysis on thepartially or wholly purified vesicle populations.

Example 27 Antibody Detection of Vesicles

Vesicles in a patient sample are assessed using antibody-coated beads todetect the vesicles in the sample using techniques as described herein.The following general protocol is used:

-   -   a. Blood is drawn from a patient at a point of care (e.g.,        clinic, doctor's office, hospital).    -   b. The plasma fraction of the blood is used for further        analysis.    -   c. To remove large particles and isolate a vesicle containing        fraction, the plasma sample is filtered, e.g., with a 0.8 or 1.2        micron (μm) syringe filter, and then passed through a size        exclusion column, e.g., with a 150 kDa molecular weight cut off.        A general schematic is shown in FIG. 87A. Filtration may be        preferable to ultracentrifugation, as illustrated in FIG. 87B.        Without being bound by theory, high-speed centrifugation may        remove protein targets weakly anchored in the membrane as        opposed to the tetraspanins which are more solidly anchored in        the membrane, and may reduce the cell specific targets in the        vesicle, which would then not be detected in subsequent analysis        of the biosignature of the vesicle.    -   d. The vesicle fraction is incubated with beads conjugated with        a “capture” antibody to a marker of interest. The captured        vesicles are then tagged with labeled “detection” antibodies,        e.g., phycoerythrin or FITC conjugated antibodies. The beads can        be labeled as well.    -   e. Captured and tagged vesicles in the sample are detected.        Fluorescently labeled beads and detection antibodies can be        detected as shown in FIG. 87C. Use of the labeled beads and        labeled detection antibodies allows assessment of beads with        vesicles bound thereto by the capture antibody.    -   f. Data is analyzed. A threshold can be set for the median        fluorescent intensity (MFI) of a particular capture antibody. A        reading for that capture antibody above the threshold can        indicate a certain phenotype. As an illustrative example, an MFI        above the threshold for a capture antibody directed to a cancer        marker can indicate the presense of cancer in the patient        sample.

In FIG. 87, the beads 816 flow through a capillary 811. Use of duallasers 812 at different wavelengths allows separate detection atdetector 813 of both the capture antibody 818 from the fluorescentsignal derived from the bead, as well as the median fluorescentintensity (MFI) resulting from the labeled detection antibodies 819. Useof labeled beads conjugated to different capture antibodies of interest,each bead labeled with a different fluor, allows for mulitiplex analysisof different vesicle 817 populations in a single assay as shown. Laser 1815 allows detection of bead type (i.e., the capture antibody) and Laser2 814 allows measurement of detector antibodies, which can includegeneral vesicle markers such as tetraspanins including CD9, CD63 andCD81. Use of different populations of beads and lasers allowssimultaneous multiplex analysis of many different populations ofvesicles in a single assay.

Example 28 Vesicle Reference Values for Prostate Cancer

Fourteen stage 3 prostate cancer subjects, eleven benign prostatehyperplasia (BPH) samples, and 15 normal samples were tested. Vesiclesamples were obtained using methods as described in Example 3 and usedin multiplexing assays as described in Examples 4 and 5. The sampleswere analyzed to determine four criteria 1) if the sample hasoverexpressed vesicles, 2) if the sample has overexpressed prostatevesicles, 3) if the sample has overexpressed cancer vesicles, and 4) ifthe sample is reliable. If the sample met all four criteria, thecategorization of the sample as positive for prostate cancer had varyingsensitivities and specificities, depending on the differentbio-signatures present for a sample as shown in Table 20.

In the table, “Vesicle” lists the threshold value or reference value ofvesicle levels, “Prostate” lists the threshold value or reference valueused for prostate vesicles, “Cancer-1,” “Cancer-2,” and “Cancer-3” liststhe threshold values or reference values for the three differentbiosignatures for prostate cancer, the “QC-1” and “QC-2” columns listthe threshold values or reference values for quality control, orreliability, and the last four columns list the specificities (“Spec”)and sensitivities (“Sens”) for benign prostate hyperplasia (BPH).

TABLE 20 Sensitivity and Specificity of Prostate Cancer BiosignaturesSens Spec Sens Spec Cancer- Cancer- Cancer- With With Without WithoutVesicle Prostate 1 2 3 QC-1 QC-2 BPH BPH BPH BPH 3000 100 na 200 n/a4000 n/a 85.70% 58.00% 85.70% 71.40% 3000 100 350 100 n/a 4000 n/a85.70% 74.10% 85.70% 85.70% 3000 100 125 125 50 4000 n/a 71.40% 83.00%71.40% 90.40% 3000 100 100 100 50 4000 8000 71.40% 87.00% 71.40% 90.40%3000 100 100 150 50 4000 n/a 64.30% 90.30% 64.20% 90.40% 3000 100 100150 150 4000 n/a 35.70% 93.40% 35.70% 95.20%

The four criteria used to categorize the samples were as follows:

Vesicle Overexpression

The median fluorescence intensities (MFIs) for a sample in three assayswas used to determine a value for the sample. Each assay used adifferent capture antibody. The first used a CD9 capture antibody, thesecond a CD81 capture antibody, and the third a CD63 antibody. The samecombination of detection antibodies was used for each assay, antibodiesfor CD9, CD81, and CD63. If the average value obtained for the threeassays was greater than 3000, the sample was categorized as havingoverexpressed vesicles (Table 20, Vesicle).

Prostate Vesicle Overexpression

The MFIs for a sample in two assays were averaged to determine a valuefor the sample. Each assay used a different capture antibody. The firstused a PCSA capture antibody and the second used a PSMA captureantibody. The same combination of labeled detection antibodies to CD9,CD81, and CD63 was used for each assay. If the average value obtainedfor the two assays was greater than 100, the sample was categorized ashaving prostate vesicles overexpressed (Table 20, Prostate).

Cancer Vesicle Overexpression

Three different cancer bio-signatures were used to determine if cancervesicles were overexpressed in a sample. The first, Cancer-1, used anEpCam capture antibody and detection antibodies for CD81, CD9, and CD63.The second, Cancer-2, used a CD9 capture antibody with detectionantibodies for EpCam and B7H3. If the MFI value of a sample for any twoof the three cancer bio-signatures was above a reference value, thesample was categorized as having overexpressed cancer (see Table 20,Cancer-1, Cancer-2, Cancer-3).

Reliability of Sample

Two quality control measures, QC-1 and QC-2, were determined for eachsample. If the sample met one of them, the sample was categorized asreliable.

For QC-1, the sum of all the MFIs of 7 assays was determined. Each ofthe 7 assays used detection antibodies for CD59 and PSMA. The captureantibody used for each assay was CD63, CD81, PCSA, PSMA, STEAP, B7H3,and EpCam. If the sum was greater than 4000, the sample was not reliableand not included.

For QC-2, the sum of all the MFIs of 5 assays was determined. Each ofthe 5 assays used detection antibodies for CD9, CD81 and CD63. Thecapture antibody used for each assay was PCSA, PSMA, STEAP, B7H3, andEpCam. If the sum was greater than 8000, the sample was not reliable andnot included.

The sensitivity and specificity for samples with BPH and without BPHsamples after a sample met the criteria are shown in Table 20.

Example 29 Detection of Prostate Cancer

High quality training set samples were obtained from commercialsuppliers. The samples comprised plasma from 42 normal prostate, 42 PCaand 15 BPH patients. The PCa samples included 4 stage III and theremainder state II. The samples were blinded until all laboratory workwas completed.

The vesicles from the samples were obtained by filtration to eliminateparticles greater than 1.5 microns, followed by column concentration andpurification using hollow fiber membrane tubes. The samples wereanalyzed using a multiplexed bead-based assay system as described above.

Antibodies to the following proteins were analyzed:

-   -   a. General Vesicle (MV) markers: CD9, CD81, and CD63    -   b. Prostate MV markers: PCSA    -   c. Cancer-Associated MV markers: EpCam and B7H3

Samples were required to pass a quality test as follows: if multiplexedmedian fluorescence intensity (MFI) PSCA+MFI B7H3+MFI EpCam <200 thensample fails due to lack of signal above background. In the trainingset, six samples (three normals and three prostate cancers) did notachieve an adequate quality score and were excluded. An upper limit onthe MFI was also established as follows: if MFI of EpCam is >6300 thentest is over the upper limit score and samples are deemed not cancer(i.e., “negative” for purposes of the test).

The samples were classified according to the result of MFI scores forthe six antibodies to the training set proteins, wherein the followingconditions must be met for the sample to be classified as PCa positive:

-   -   a. Average MFI of General MV markers >1500    -   b. PCSA MFI >300    -   c. B7H3 MFI >550    -   d. EpCam MFI between 550 and 6300

Using the 84 normal and PCa training data samples, the test was found tobe 98% sensitive and 95% specific for PCa vs normal samples. See FIG.88A. The increased MFI of the PCa samples compared to normals is shownin FIG. 88B. The sensitivity and specificity of the test compared toconventional PSA and PCA3 are presented in FIGS. 89A and 89B,respectively. Compared to PSA and PCA3 testing, the PCa Test presentedin this Example can result in saving 220 men without PCa in every 1000normal men screened from having an unnecessary biopsy.

Example 30 Differentiating BPH from PCa

BPH is a common cause of elevated PSA levels. PSA can only indicatedwhether there is something wrong with the prostate, but it cannoteffectively differentiate between BPH and PCa. PCA3, a transcript foundto be overexpressed by prostate cancer cells, is thought to be slightlymore specific for PCa, but this depends on the cutoffs used for PSA andPCA3, as well as the populations studied.

BPH can be characterized by vesicle (MV) analysis. Examining the samplesdescribed in Example 29, ten out of the 15 BPH samples (67%) have higherlevels of CD63+ vesicles than the PCa samples, including the stage IIIs.See FIG. 90. Also, 14 out of 15 BPH (93%) have higher levels of CD63+vesicles than the normals. This indicates that an inflammation-specificsignature that differs from cancer may be used in differentiating BPHfrom PCa.

The PCa test as in Example 29 was repeated including the 15 BPH samples.Using all 99 samples, the test was 98% sensitive and 84% specific. SeeFIG. 91. Thus, the test provides a 15% improvement over PSA. Performancevalues for PSA and PCA3 are commonly reported for settings without BPHin their cohorts, nevertheless, the vesicle test of the invention stilloutperforms conventional testing even when BPH was included. See FIG.92. In this setting, the PCa test of the invention results in saving 110men in every 1000 men without PCa screened from having an unnecessarybiopsy as compared to PSA testing. And of those men biopsied due to apositive result from the assay, most will have something wrong withtheir prostate because the test performs well at identifying normal men(i.e., 95% specific in that population, see Example 29).

FIG. 93 presents ROC curve analysis of the vesicle assays of theinvention versus conventional testing. When the ROC curve climbs rapidlytowards upper left hand corner of the graph, the true positive rate ishigh and the false positive rate (1−specificity) is low. The AUCcomparison shown in FIG. 93 shows that the test of the invention is muchmore likely to correctly classify a sample than conventional PSA or PCA3testing.

FIG. 94 shows that there is a correlation between general vesicle (MV)levels, levels of prostate-specific MVs and MVs with cancer markers,indicating these markers are correlated in the subject populations. Suchcancer specific markers can be further used to differentiate between BPHand PCa. In the figure, General MV markers include CD9, CD63 and CD81;Prostate MV markers include PCSA and PSMA; and Cancer MV markers includeEpCam and B7H3. Testing of PCa samples without the vesicle capturemarkers revealed sensitivity and specificity values nearly the same asthose with the general MV markers were used. Similarly, detection ofcancer without using B7H3 only leads to minimal reduction inperformance. These data reveal that the markers of the invention can besubstituted and tested in various configurations to still achieveoptimal assay performance.

FIG. 95 shows additional markers that can distinguish between PCa andnormal samples that can be added to improve test performance. The figureshows the median fluorescence intensity (MFI) levels of vesiclescaptured with ICAM1, EGFR, STEAP1 and PSCA and labeled withphycoerythrin-labeled antibodies to tetraspanins CD9, CD63 and CD81.

Example 31 Microsphere Vesicle Prostate Cancer Assay Protocol

In this example, the vesicle PCa test is a microsphere based immunoassayfor the detection of a set of protein biomarkers present on the vesiclesfrom plasma of patients with prostate cancer. The test employs specificantibodies to the following protein biomarkers: CD9, CD59, CD63, CD81,PSMA, PCSA, B7H3 and EpCAM. After capture of the vesicles by antibodycoated microspheres, phycoerythrin-labeled antibodies are used for thedetection of vesicle specific biomarkers. See FIG. 96A. Depending on thelevel of binding of these antibodies to the vesicles from a patient'splasma a determination of the presence or absence of prostate cancer ismade.

Vesicles are isolated as described in Example 7.

Microspheres

Specific antibodies are conjugated to microspheres (Luminex) after whichthe microspheres are combined to make a Microsphere Master Mixconsisting of L100-C105-01; L100-C115-01; L100-C119-01; L100-C120-01;L100-C122-01; L100-C124-01; L100-C135-01; and L100-C175-01. xMAP®Classification Calibration Microspheres L100-CAL1 (Luminex) are used asinstrument calibration reagents for the Luminex LX200 instrument. xMAP®Reporter Calibration Microspheres L100-CAL2 (Luminex) are used asinstrument reporter calibration reagents for the Luminex LX200instrument. xMAP® Classification Control Microspheres L100-CON1(Luminex) are used as instrument control reagents for the Luminex LX200instrument. xMAP Reporter Control Microspheres L100-CON2 (Luminex) andare used as reporter control reagents for the Luminex LX200 instrument.

Capture Antibodies

The following antibodies are used to coat Luminex microspheres for usein capturing certain populations of vesicles by binding to theirrespective protein targets on the vesicles in this Example: a. Mouseanti-human CD9 monoclonal antibody is an IgG2b used to coat microsphereL100-C105 to make *EPCLMACD9-C105; b. Mouse anti-human PSMA monoclonalantibody is an IgG1 used to coat microsphere L100-C115 to makeEPCLMAPSMA-C115; c. Mouse anti-human PCSA monoclonal antibody is an IgG1used to coat microsphere L100-C119 to make EPCLMAPCSA-C119; d. Mouseanti-human CD63monoclonal antibody is an IgG1 used to coat microsphereL100-C120 to make EPCLMACD63-C120; e. Mouse anti-human CD81 monoclonalantibody is an IgG1 used to coat microsphere L100-C124 to makeEPCLMACD81-C124; f. Goat anti-human B7-H3 polyclonal antibody is an IgGpurified antibody used to coat microsphere L100-C125 to makeEPCLGAB7-H3-C125; and g. Mouse anti-human EpCAM monoclonal antibody isan IgG2b purified antibody used to coat microsphere L100-C175 to makeEPCLMAEpCAM-C175.

Detection Antibodies

The following phycoerythrin (PE) labeled antibodies are used asdetection probes in this assay: a. EPCLMACD81PE: Mouse anti-human CD81PE labeled antibody is an IgG1 antibody used to detect CD81 on capturedvesicles; b. EPCLMACD9PE: Mouse anti-human CD9 PE labeled antibody is anIgG1 antibody used to detect CD9 on captured vesicles; c. EPCLMACD63PE:Mouse anti-human CD63 PE labeled antibody is an IgG1 antibody used todetect CD63 on captured vesicles; d. EPCLMAEpCAMPE: Mouse anti-humanEpCAM PE labeled antibody is an IgG1 antibody used to detect EpCAM oncaptured vesicles; e. EPCLMAPSMAPE: Mouse anti-human PSMA PE labeledantibody is an IgG1 antibody used to detect PSMA on captured vesicles;f. EPCLMACD59PE: Mouse anti-human CD59 PE labeled antibody is an IgG1antibody used to detect CD59 on captured vesicles; and g. EPCLMAB7-H3PE:Mouse anti-human B7-H3 PE labeled antibody is an IgG1 antibody used todetect B7-H3 on captured vesicles.

Reagent Preparation

Antibody Purification:

The following antibodies in Table 21 are received from vendors andpurified and adjusted to the desired working concentrations according tothe following protocol.

TABLE 21 Antibodies for PCa Assay Antibody Use EPCLMACD9 Coating ofmicrospheres for vesicle capture EPCLMACD63 Coating of microspheres forvesicle capture EPCLMACD81 Coating of microspheres for vesicle captureEPCLMAPSMA Coating of microspheres for vesicle capture EPCLGAB7-H3Coating of microspheres for vesicle capture EPCLMAEpCAM Coating ofmicrospheres for vesicle capture EPCLMAPCSA Coating of microspheres forvesicle capture EPCLMACD81PE PE coated antibody for vesicle biomarkerdetection EPCLMACD9PE PE coated antibody for vesicle biomarker detectionEPCLMACD63PE PE coated antibody for vesicle biomarker detectionEPCLMAEpCAMPE PE coated antibody for vesicle biomarker detectionEPCLMAPSMAPE PE coated antibody for vesicle biomarker detectionEPCLMACD59PE PE coated antibody for vesicle biomarker detectionEPCLMAB7-H3PE PE coated antibody for vesicle biomarker detection

Antibody Purification Protocol: Antibodies are purified using Protein Gresin from Pierce (Protein G spin kit, prod #89979).Micro-chromatography columns made from filtered P-200 tips are used forpurification.

One hundred μl of Protein G resin is loaded with 100 μl buffer from thePierce kit to each micro column. After waiting a few minutes to allowthe resin to settle down, air pressure is applied with a P-200 Pipettmanto drain buffer when needed, ensuring the column is not let to dry. Thecolumn is equilibrated with 0.6 ml of Binding Buffer (pH 7.4, 100 mMPhosphate Buffer, 150 mM NaCl; (Pierce, Prod #89979). An antibody isapplied to the column (<1 mg of antibody is loaded on the column). Thecolumn is washed with 1.5 ml of Binding Buffer. Five tubes (1.5 ml microcentrifuge tubes) are prepared and 10 μl of neutralization solution(Pierce, Prod #89979) is applied to each tube. The antibody is elutedwith the elution buffer from the kit to each of the five tubes, 100 ulfor each tube (for a total of 500 μl). The relative absorbance of eachfraction is measured at 280 nm using Nanodrop (Thermo scientific,Nanodrop 1000 spectrophotometer). The fractions with highest OD readingare selected for downstream usage. The samples are dialyzed against 0.25liters PBS buffer using Pierce Slide-A-Lyzer Dialysis Cassette (Pierce,prod 66333, 3 KDa cut off). The buffer is exchanged every 2 hours forminimum three exchanges at 4° C. with continuous stirring. The dialyzedsamples are then transferred to 1.5 ml microcentifuge tubes, and can belabeled and stored at 4° C. (short term) or −20° C. (long term).

Microsphere Working Mix Assembly:

A microsphere working mix MWM101 includes the first four rows ofantibody, microsphere and coated microsphere of Table 22.

TABLE 22 Antibody-Microsphere Combinations Antibody Microsphere CoatedMicrosphere EPCLMACD9 L100-C105 EPCLMACD9-C105 EPCLMACD63 L100-C120EPCLMACD63-C120 EPCLMACD81 L100-C124 EPCLMACD81-C124 EPCLMAPSMAL100-C115 EPCLMAPSMA-C115 EPCLGAB7-H3 L100-C125 EPCLGAB7-H3-C125bEPCLMAEpCAM L100-C175 EPCLMAEpCAM-C175 EPCLMAPCSA L100-C119EPCLMAPCSA-C119

Microspheres are coated with their respective antibodies as listed aboveaccording to the following protocol.

Protocol for Two-Step Carbodiimide Coupling of Protein to CarboxylatedMicrospheres:

The microspheres should be protected from prolonged exposure to lightthroughout this procedure. The stock uncoupled microspheres areresuspended according to the instructions described in the ProductInformation Sheet provided with the microspheres (xMAP technologies,MicroPlex™ Microspheres). Five ×106 of the stock microspheres aretransferred to a USA Scientific 1.5 ml microcentrifuge tube. The stockmicrospheres are pelleted by microcentrifugation at ≧8000×g for 1-2minutes at room temperature. The supernatant is removed and the pelletedmicrospheres are resuspended in 100 μl of dH2O by vortex and sonicationfor approximately 20 seconds. The microspheres are pelleted bymicrocentrifugation at ≧8000×g for 1-2 minutes at room temperature. Thesupernatant is removed and the washed microspheres are resuspended in 80μl of 100 mM Monobasic Sodium Phosphate, pH 6.2 by vortex and sonication(Branson 1510, Branson UL Trasonics Corp.) for approximately 20 seconds.Ten μl of 50 mg/ml Sulfo-NHS (Thermo Scientific, Cat#24500) (diluted indH20) is added to the microspheres and is mixed gently by vortex. Ten μlof 50 mg/ml EDC (Thermo Scientific, Cat#25952-53-8) (diluted in dH20) isadded to the microspheres and gently mixed by vortexing. Themicrospheres are incubated for 20 minutes at room temperature withgentle mixing by vortex at 10 minute intervals. The activatedmicrospheres are pelleted by microcentrifugation at ≧8000×g for 1-2minutes at room temperature. The supernatant is removed and themicrospheres are resuspended in 250 μl of 50 mM MES, pH 5.0 (MES, Sigma,Cat# M2933) by vortex and sonication for approximately 20 seconds. (OnlyPBS-1% BSA+Azide (PBS-BN) ((Sigma (P3688-10PAK+0.05% NaAzide (S8032)))should be used as assay buffer as well as wash buffer.). Themicrospheres are then pelleted by microcentrifugation at ≧8000×g for 1-2minutes at room temperature.

The supernatant is removed and the microspheres are resuspended in 250μl of 50 mM MES, pH 5.0 (MES, Sigma, Cat# M2933) by vortex andsonication for approximately 20 seconds. (Only PBS-1% BSA+Azide (PBS-BN)((Sigma (P3688-10PAK+0.05% NaAzide (S8032))) should be used as assaybuffer as well as wash buffer.). The microspheres are then pelleted bymicrocentrifugation at ≧8000×g for 1-2 minutes at room temperature, thuscompleting two washes with 50 mM MES, pH 5.0.

The supernatant is removed and the activated and washed microspheres areresuspended in 100 μl of 50 mM MES, pH 5.0 by vortex and sonication forapproximately 20 seconds. Protien in the amount of 125, 25, 5 or 1 μg isadded to the resuspended microspheres. (Note: Titration in the 1 to 125μg range can be performed to determine the optimal amount of protein perspecific coupling reaction.). The total volume is brought up to 500 μlwith 50 mM MES, pH 5.0. The coupling reaction is mixed by vortex and isincubated for 2 hours with mixing (by rotating on Labquake rotator,Barnstead) at room temperature. The coupled microspheres are pelleted bymicrocentrifugation at ≧8000×g for 1-2 minutes at room temperature. Thesupernatant is removed and the pelleted microspheres are resuspended in500 μL of PBS-TBN by vortex and sonication for approximately 20 seconds.(Concentrations can be optimized for specific reagents, assayconditions, level of multiplexing, etc. in use.).

The microspheres are incubated for 30 minutes with mixing (by rotatingon Labquake rotator, Barnstead) at room temperature. The coupledmicrospheres are pelleted by microcentrifugation at ≧8000×g for 1-2minutes at room temperature. The supernatant is removed and themicrospheres are resuspended in 1 ml of PBS-TBN by vortex and sonicationfor approximately 20 seconds. (Each time there is the addition ofsamples, detector antibody or SA-PE the plate is covered with a sealerand light blocker (such as aluminum foil), placed on the orbital shakerand set to 900 for 15-30 seconds to re-suspend the beads. Following thatthe speed should be set to 550 for the duration of the incubation.).

The microspheres are pelleted by microcentrifugation at ≧8000×g for 1-2minutes. The supernatant is removed and the microspheres are resuspendedin 1 ml of PBS-TBN by vortex and sonication for approximately 20seconds. The microspheres are pelleted by microcentrifugation at ≧8000×gfor 1-2 minutes (resulting in a total of two washes with 1 ml PBS-TBN).

Protocol for Microsphere Assay:

The preparation for multiple phycoerythrin detector antibodies is usedas described in Example 4. One hundred μl is analyzed on the Luminexanalyzer (Luminex 200, xMAP technologies) according to the system manual(High PMT setting).

Decision Tree:

A decision tree (FIG. 96B-D) is used to assess the results from themicrosphere assay to determine if a subject has cancer. Threshold limitson the MFI is established and samples classified according to the resultof MFI scores for the antibodies, to determine whether a sample hassufficient signal to perform analysis (e.g., is a valid sample foranalysis or an invalid sample for further analysis, in which case asecond patient sample may be obtained) and whether the sample is PCapositive. FIG. 96B shows a decision tree using the MFI obtained withCD59, PSMA, PCSA, B7-H3, EpCAM, CD9, CD81 and CD63. FIG. 96C shows adecision tree using the MFI obtained with PSMA, B7-H3, EpCAM, CD9, CD81and CD63. A sample is classified as indeterminate if the MFI is withinthe standard deviation of the predetermined threshold. For validation,the sample must have sufficient signal when capturing vesicles with theindividual tetraspanins and labeling with all tetraspanins. A samplethat passes validation is called positive if the prostate-specificmarker (PSMA) is considered positive and the joint signal of the cancermarkers (B7-H3 and EpCam) is also considered positive. FIG. 96D shows adecision tree using the MFI obtained with PCSA, PSMA, B7-H3, CD9, CD81and CD63. A sample is classified as indeterminate if the MFI is withinthe standard deviation of the predetermined threshold (TH). In thiscase, a second patient sample can be obtained. For validation, thesample must have sufficient signal when capturing vesicles with theindividual tetraspanins and labeling with all tetraspanins. A samplethat passes validation is called positive if either of theprostate-specific markers (PSMA or PCSA) is considered positive, and thecancer marker (B7-H3) is also considered positive.

Results:

See Examples 32 and 33.

Example 32 Microsphere Vesicle PCa Assay Performance

In this example, the vesicle PCa test is a microsphere based immunoassayfor the detection of a set of protein biomarkers present on the vesiclesfrom plasma of patients with prostate cancer. The test is performedsimilarly to that of Example 31 with modifications indicated below.

The test uses a multiplexed immunoassay designed to detect circulatingmicrovesicles. The test uses PCSA, PSMA and B7H3 to capture themicrovesicles present in patient samples such as plasma and uses CD9,CD81, and CD63 to detect the captured microvesicles. The output of thisassay is the median fluorescent intensity (MFI) that results from theantibody capture and fluorescently labeled antibody detection ofmicrovesicles that contain both the individual capture protein and thedetector proteins on the microvesicle. A sample is “POSITIVE” by thistest if the MFI levels of PSMA or PCSA, and B7H3 protein-containingmicrovesicles are above the empirically determined threshold. A sampleis determined to be “NEGATIVE” if any one of these two microvesiclecapture categories exhibit an MFI level that is below the empiricallydetermined threshold. Alternatively, a result of “INDETERMINATE” will bereported if the sample MFI fails to clearly produce a positive ornegative result due to MFI values not meeting certain thresholds or thereplicate data showed too much statistical variation. A “NON-EVALUABLE”interpretation for this test indicates that this patient samplecontained inadequate microvesicle quality for analysis. See Example 33for a method to determine the empirically derived threshold values.

The test employs specific antibodies to the following proteinbiomarkers: CD9, CD59, CD63, CD81, PSMA, PCSA, and B7H3 as in Example31. Decision rules are set to determine if a sample is called positive,negative or indeterminate, as outlined in Table 23. See also Example 31.For a sample to be called positive the replicates must exceed all fourof the MFI cutoffs determined for the tetraspanin markers (CD9, CD63,CD81), prostate markers (PSMA or PCSA), and B7H3. Samples are calledindeterminate if both of the three replicates from PSMA and PCSA or anyof the three replicates from B7H3 antibodies span the cutoff MFI value.Samples are called negative if there is at least one of the tetraspaninmarkers (CD9, CD63, and CD81), prostate markers (PSMA or PCSA), B7H3that fall below the MFI cutoffs.

TABLE 23 MFI Parameter for Each Capture Antibody Tetraspanin MarkersProstate Markers Result (CD9, CD63, CD81) (PSMA, PCSA) B7H3Determination Average of all All replicates from All replicates from Ifall 3 are true, replicates from the either of the two B7H3 have a MFIthen the sample is three tetraspanins have prostate markers have >300called Positive a MFI >500 a MFI >350 for PCSA and >90 for PSMA Bothreplicate sets Any replicates If either are true, from either prostatefrom B7H3 have then the sample is marker have values values both abovecalled both above and below and below a MFI = indeterminate a MFI = 350for PCSA 300 and = 90 for PSMA All replicates from the All replicatesfrom All replicates from If any of the 3 are three tetraspanins haveeither of the two B7H3 have a MFI = true, then the a MFI <500 prostatemarkers have <300 sample is called a MFI <350 for PCSA Negative, giventhe and <90 for PSMA sample doesn't qualify as indeterminate

The vesicle PCa test was compared to elevated PSA on a cohort of 296patients with or without PCa as confirmed by biopsy. An ROC curve of theresults is shown in FIG. 97A. As shown, the area under the curve (AUC)for the vesicle PCa test was 0.94 whereas the AUC for elevated PSA onthe same samples was only 0.68. The PCa samples were likely found due toa high PSA value. Thus this population is skewed in favor of PSA,accounting for the higher AUC than is observed in a true clinicalsetting.

The vesicle PCa test was further performed on a cohort of 933 patientplasma samples. Results are shown in FIG. 97B and are summarized inTable 24:

TABLE 24 Performance of vesicle PCa test on 933 patient cohort TruePositive 409 True Negative 307 False Positive 50 False Negative 72Non-evaluable 63 Indeterminate 32 Total 933 Sensitivity 85% Specificity86% Accuracy 85% Non-evaluable Rate  8% Indeterminate Rate  5%

As shown in Table 24, the vesicle PCa test achieved an 85% sensitivitylevel at a 86% specificity level, for an accuracy of 85%. In contrast,PSA at a sensitivity of 85% had a specificity of about 55%, and PSA at aspecificity of 86% had a sensitivity of about 5%. FIG. 97A. About 12% ofthe 933 samples were non-evaluable or indeterminate. Samples from thepatients could be recollected and re-evaluated. FIG. 97C shows an ROCcurve corresponding to the data shown in FIG. 97B. The vesicle PCa testhad an AUC of 0.92 for the 933 samples.

Example 33 Median Fluorescence Intensity (MFI) Threshold Calculations

It is common to set a threshold level for a biomarker, wherein valuesabove or below the threshold signify differential results, e.g.,positive versus negative results. For example, the standard for PSA is athreshold of 4 ng/ml of PSA in serum. PSA levels below this thresholdare considered normal whereas PSA values above this threshold mayindicate a problem with the prostate, e.g., BPH or prostate cancer(PCa). The threshold can be adjusted to favor enhanced sensitivityversus specificity. In the case of PSA, a lower threshold would detectmore cancers, and thus increase sensitivity, but would concomitantlyincrease the number of false positives, and thus decrease specificity.Similarly, a higher threshold would detect fewer cancers, and thusdecrease sensitivity, but would concomitantly decrease the number offalse positives, and thus increase specificity.

In the Examples herein such as Examples 29-32, threshold MFI values areset for the vesicle biomarkers to construct a test for detecting PCa.This Example provides an approach to determining the threshold values.To this end, cluster analysis was used to determine if there were PCapositive and negative populations that could be separated based on MFIthreshold values that result in the desired level of sensitivity.

Fluorescence intensity values are exponentially distributed, thus priorto performing the clustering analysis, the data was logarithmicallytransformed. The resulting data set was subjected to traditional hardclustering methods. The hard clustering implemented here uses definedEuclidean distance parameter to determine if a data point belongs to aparticular cluster. The algorithm used allocates each data point to oneof c clusters to minimize the within-cluster sum of squares:

$\sum\limits_{i = 1}^{c}\; {\sum\limits_{k \in A_{i}}^{\;}\; {{x_{k} - v_{i}}}_{2}}$

where A_(i) is a set of objects (data points) in the i-th cluster andv_(i) is the mean for those points over cluster i. This equation denotesa Euclidian distance norm. The data from 149 samples was used todetermine the clusters. The raw data was logarithmically transformed sothat it was uniformly distributed. The data was then normalized bysubtracting the minimum value and dividing by the maximum. Plots of PSMAvs B7H3, PCSA vs B7H3, and PSMA vs PCSA both before and aftertransformation are shown in FIG. 98A.

Each possible combination of markers was analyzed, PSMA vs B7H3, PCSA vsPSMA, and PCSA vs B7H3 and thresholds determined to best separate thepopulations identified. Horizontal and vertical lines where found thatbest separated the two clusters. The point where the line crossed theaxis was used to define the cutoff, which required first that the valuebe denormalized, then the antilog taken. This resulted in cutoffs of 90and 300 for each PSMA vs B7H3 respectively, as shown in shown in FIG.98B.

For PCSA vs B7H3, the two clusters found are shown in FIG. 98C.Horizontal and vertical lines where found that best separated the twoclusters. The point that the line crossed the axis was used to definethe cutoff, which required first that the value be denormalized, thenthe antilog taken. This resulted in cutoffs of 430 and 300 for each PCSAvs B7H3 respectively.

For PSMA vs PCSA, the two clusters found are shown in FIG. 98D.Horizontal and vertical lines where found that best separated the twoclusters. The point that the line crossed the axis was used to definethe cutoff, which required first that the value be denormalized, thenthe antilog taken. This resulted in in cutoffs of 85 and 350 for eachPSMA vs PCSA respectively.

Sensitivity and specificity were calculated for all combinations ofthresholds found with the cluster analysis. There was no change insensitivity or specificity with values of 85 or 90 for PSMA, thus 90 waschosen to use as the cutoff. There was no change in sensitivity withthresholds of 430 or 350 for PCSA, though specificity decreased by 0.3%with the change. Since this is an insignificant change, a value of 350was chosen for the PCSA cutoff so as to err on the side of highersensitivity. Both clusters had the same threshold of 300 for B7H3, sothis value was used. The resulting sensitivity and specificity withthese threshold values was 92.7% and 81.8% respectively.

These thresholds were applied to the larger set of data containing 313samples, and resulted in a sensitivity of 92.8% and a specificity of78.7%. See FIG. 98E.

The thresholds in this Example were determined in a fashion that wasindependent of whether the samples were from normal or cancer patients.Since the thresholds perform well at separating the two populations, itis likely that there are in fact two separate underlying populations dueto differences in the biology of the specimens. This difference ishighly correlated to the presence or absence of prostate cancer, andthus serves as a good recommendation for the performance of a biopsy.The method is used to determine MFI thresholds for any desiredcomparison such as detecting other cancers from normal.

Example 34 Vesicle PCa Protein Marker Discovery

In this example, vesicle protein biomarkers were assessed as above.Samples were derived from a total of 522 patients, including 285prostate cancer samples and 237 controls. Markers tested included CD9,PSMA, PCSA, CD63, CD81, B7H3, IL6, OPG-13, IL6R, PA2G4, EZH2, RUNX2,SERPINB3 and EpCam. Results are shown in FIG. 99, which shows meanfluorescence intensity (MFI) values for prostate cancer and normalsamples for each marker tested. Higher levels of the vesicle surfacemarkers were observed for all markers except the tetraspanins (e.g.,CD63 and CD81), which serve a general vesicle biomarkers. Performance ofthe various markers and combinations thereof is shown in Table 25:

TABLE 25 Performance of various markers and marker combinations Marker/sSensitivity Specificity PSMA 84% 81% PCSA 87% 82% B7-H3 81% 85% IL 6 83%82% OPG-13 91% 81% IL6R 84% 81% PA2G4 90% 80% EZH2 81% 89% RUNX2 94% 74%SERPINB3 96% 68% OPG + PA2G4 + RUNX2 + SERPI 93% 81% PCSA + B7H3 81% 87%PA2G4 + RUNX2 + SERPI 88% 82% OPG + RUNX2 + SERPI 88% 85% OPG + PA2G4 +SERPI 86% 85% OPG + PA2G4 + RUNX2 85% 86% OPG + PA2G4 + RUNX2 + SERPI +94% 79% PCSA + B7H3

Analysis of various markers and combinations thereof as in the Exampleis used for the discovery of vesicle markers for detection of disease.In addition to assay performance, selection of antibodies can beinfluenced by perception of the markers in the medical community,availability of reagents, ability to multiplex, and other factors.

Example 35 Vesicle Protein Array to Detect Prostate Cancer

In this example, the vesicle PCa test is performed using a proteinarray, more specifically an antibody array, for the detection of a setof protein biomarkers present on the vesicles from plasma of patientswith prostate cancer. The array comprises capture antibodies specific tothe following protein biomarkers: CD9, CD59, CD63, CD81. Vesicles areisolated as described above, e.g., in Examples 3 and 7. After filtrationand isolation of the vesicles from plasma of men at risk for PCa, suchas those over the age of 50, the plasma samples are incubated with anarray harboring the various capture antibodies. Depending on the levelof binding of fluorescently labeled detection antibodies to PSMA, PCSA,B7H3 and EpCAM that bind to the vesicles from a patient's plasma thathybridize to the array, a determination of the presence or absence ofprostate cancer is made.

In a second array format, the vesicles are isolated from plasma andhybridized to an array containing CD9, CD59, CD63, CD81, PSMA, PCSA,B7H3 and EpCam. The captured vesicles are tagged with non-specificvesicle antibodies labeled with Cy3 and/or Cy5. The fluorescence isdetected. Depending on the pattern of binding, a determination of thepresence or absence of prostate cancer is made.

Example 36 Distinguishing BPH and PCa on Antibody Arrays

Concentrated plasma containing vesicles from 8 BPH and 8 prostate cancerstage III patients was diluted 1:30 and hybridized to a Raybiotech HumanReceptor array containing 40 human cytokine receptors. The averageconcentration (pg/ml) of each cytokine for each group was compared usingan unpaired t-test. Of the 40 receptors tested, Trappin-2, Ceacam-1,HVEM, IL-10Rb, IL-1 R4 and BCMA were the most significantlydifferentially expressed. See FIG. 100.

A t-test was used to determine statistical significance of thedifferential expression. Results are shown in Table 26:

TABLE 26 Differentiation of BPH and PCa using antibody markers Markerp-value Trappin-2 0.018 Ceacam-1 0.013 HVEM 0.0095 IL-10Rb 0.052 IL-1 R40.054 BCMA 0.019

By using a combination of the 6 markers to differentiate BPH and PCa, asensitivity of 100% with 87.5% specificity for BPH was obtained.

Example 37 Distinguishing BPH and PCa Using miRs

RNA from the plasma derived vesicles of nine normal male individuals andnine individuals with stage 3 prostate cancers were analyzed on theExiqon mIRCURY LNA microRNA PCR system panel. The Exiqon 384 well panelsmeasure 750 miRs. Samples were normalized to control primers towardssynthetic RNA spike-in from Universal cDNA synthesis kit (UniSp6 CP).Normalized values for each probe across three data sets for eachindication (BPH or PCa) were averaged. Probes with an average CV %higher than 20% were not used for analysis.

Analysis of the results revealed several microRNAs that were 2 fold ormore over-expressed in BPH samples compared to Stage 3 prostate cancersamples. These miRs include: hsa-miR-329, hsa-miR-30a, hsa-miR-335,hsa-miR-152, hsa-miR-151-5p, hsa-miR-200a and hsa-miR-145, as shown inTable 27:

TABLE 27 miRs overexpressed in BPH vs PCa Overexpressed in BPH v PCaFold Change hsa-miR-329 12.32 hsa-miR-30a 6.16 hsa-miR-335 6.00hsa-miR-152 4.73 hsa-miR-151-5p 3.16 hsa-miR-200a 3.16 hsa-miR-145 2.35

In addition, a number of miRs were overexpressed at least 2-fold in PCaversus BPH. These miRs include: hsa-miR-29a, hsa-miR-106b, hsa-miR-595,hsa-miR-142-5p, hsa-miR-99a, hsa-miR-20b, hsa-miR-373, hsa-miR-502-5p,hsa-miR-29b, hsa-miR-142-3p, hsa-miR-663, hsa-miR-423-5p, hsa-miR-15a,hsa-miR-888, hsa-miR-361-3p, hsa-miR-365, hsa-miR-10b, hsa-miR-199a-3p,hsa-miR-181a, hsa-miR-19a, hsa-miR-125b, hsa-miR-760, hsa-miR-7a,hsa-miR-671-5p, hsa-miR-7c, hsa-miR-1979, and hsa-miR-103, as shown inTable 28:

TABLE 28 miRs overexpressed in PCa vs BPH Overexpressed in PCa v BPHFold Change hsa-miR-29a 2.18 hsa-miR-106b 2.23 hsa-miR-595 2.24hsa-miR-142-5p 2.25 hsa-miR-99a 2.30 hsa-miR-20b 2.36 hsa-miR-373* 2.37hsa-miR-502-5p 2.39 hsa-miR-29b 2.43 hsa-miR-142-3p 2.44 hsa-miR-6632.51 hsa-miR-423-5p 2.55 hsa-miR-15a 2.71 hsa-miR-888 2.72hsa-miR-361-3p 2.86 hsa-miR-365 2.90 hsa-miR-10b 2.90 hsa-miR-199a-3p2.96 hsa-miR-181a 3.00 hsa-miR-19a 3.03 hsa-miR-125b 3.05 hsa-miR-7603.10 hsa-miR-7a 3.77 hsa-miR-671-5p 4.11 hsa-miR-7c 5.56 hsa-miR-19795.80 hsa-miR-103 6.42

Example 38 miR-145 in Controls and PCa Samples

FIG. 101 illustrates a comparison of miR-145 in control and prostatecancer samples. RNA was collected as in Example 13. The controls includeCaucasians >75 years old and African Americans >65 years old with PSA <4ng/ml and a benign digital rectal exam (DRE). As seen in the figure,miR-145 was under expressed in PCa samples. miR-145 is useful foridentifying those with early/indolent PCa vs those with benign prostatechanges (e.g., BPH).

Example 39 Discovery of miRs Differentially Expressed in Metastatic andNon-Metastatic PCa

A panel of 720 miRs was used to compare miR expression in plasma samplesfrom 48 patients with identified non-metastatic prostate cancer and 19patients with identified metastatic prostate cancer. RNA derived frommicrovesicles of the plasma samples was evaluated on the Exiqon microRNAready to use qRT-PCR panel version 1.0. Results were normalized tointer-plate calibrator probes and then subjected to a paired t-test.P-values were corrected with a Benjamini and Hochberg false-discoveryrate test.

Table 29 shows the top four most upregulated and top four mostdownregulated miRs so identified. Among other target mRNAs, miR-145 ispredicted to regulate BRAF, a well-characterized oncogene. miR-32 andmiR-134 are predicted to regulate SMAD6, which is associated withnegative regulation of BMP and TGF-beta/activin-signaling. SMAD6expression has been associated with poor cancer prognosis.

TABLE 29 miR expression in metastatic versus non-metastatic PCa samplesSignificantly Up or Down Associated different miRs (p- Fold Regulated inGene value <0.01) Change Metastatic Disease p-value Target miR-495 117.1Up 0.0021 BRAF miR-10a 16.4 Up 0.0050 miR-30a 11.4 Up 0.0067 miR-57011.0 Up 0.0042 miR-32 35.0 Down 0.0042 SMAD6 miR-885-3p 9.5 Down 0.0042miR-564 4.3 Down 0.0066 miR-134 3.7 Down 0.0086 SMAD6

The experimental setup above was repeated using Exiqon microRNA ready touse qRT-PCR panel version 2.0 and plasma samples from a cohort of 10patients with metastatic prostate cancer and 17 patients withnon-metastatic prostate cancer. Non-corrected p-values for the mostsignificantly differently expressed miRs are shown in Table 30.

TABLE 30 miR expression in metastatic versus non-metastatic PCa samplesRegulation MicroRNA p-value in non metastatic Fold Change hsa-miR-3751.22E−04 down 36.7 hsa-miR-452 2.04E−04 down 10.3 hsa-miR-200b 0.00207down 5.5 hsa-miR-146b-5p 0.00241 down 9.7 hsa-miR-1296 0.00430 down 4.5hsa-miR-17* 0.0104 down 5.6 hsa-miR-100 0.0207 down 4.5 hsa-miR-574-3p0.0222 down 2.6 hsa-miR-20a* 0.0259 down 2.3 hsa-miR-572 0.0281 up 6.5hsa-miR-1236 0.0313 down 2.5 hsa-miR-181a 0.0374 down 9.5 hsa-miR-9370.0446 up 5.7 hsa-miR-23a* 0.0474 down 2.0

Example 40 Detection of miRs Differentially Expressed in PCa

A panel of miRs was used to compare miR expression in plasma samplesfrom 28 men without prostate cancer and 64 men with prostate cancer. Inall cases, prostate cancer status was confirmed by biopsy. RNA derivedfrom microvesicles of the plasma samples was evaluated on the ExiqonmicroRNA ready to use qRT-PCR panel. Results were normalized tointer-plate calibrator probes and then subjected to a paired t-test.P-values were corrected with a Benjamini and Hochberg false-discoveryrate test. P-values for the most significantly differently expressedmiRs are shown in Table 31.

TABLE 31 miR expression in metastatic versus non-metastatic PCa samplesRegulation in MicroRNA p-value controls Fold Change hsa-miR-574-3p0.0264 down 3.52 hsa-miR-331-3p 0.0264 down 6.63 hsa-miR-326 0.0264 down5.99 hsa-miR-181a-2* 0.0264 up 2.95 hsa-miR-130b 0.0264 down 4.83hsa-miR-301a 0.0264 down 8.18 hsa-miR-141 0.0312 down 4.13 hsa-miR-4320.0351 down 3.83 hsa-miR-107 0.0351 down 8.29 hsa-miR-628-5p 0.0391 up3.25 hsa-miR-625* 0.0391 down 3.98 hsa-miR-497 0.0495 down 4.35hsa-miR-484 0.0495 down 2.90

Example 41 miRs Differentially Expressed in PCa

A panel of 84 prostate cancers and 28 control samples (biopsy controlledwithout prostate cancer) were analyzed using Exiqon RT-PCR panels asdescribed herein. Using the TNM scale, the prostate cancers included 13MX samples (did not evaluate distant metastasis), 55 M0 samples (nodistant metastasis), and 16 M1 samples (confirmed distant metastasis).

miRs were detected in vesicles isolated from the patient samples. RNAwas isolated from 150 μl of frozen plasma concentrate from each sampleusing a modified Qiagen miRneasy protocol (Qiagen GmbH, Germany). Themodified protocol included treating the concentrated samples with RnaseA before isolation so that only RNA protected within vesicles wasanalyzed in each sample. The samples were spiked with a known quantityof C. elegans microRNA for normalization in subsequent steps. 40 ng ofRNA isolated from vesicles in the sample was used for each Exiqon panel.

The Exiqon RT-PCR panel consisted of two 384 cards covering 750 miRs andcontrol assays. The qRT-PCR assay was performed using a Sybr green assayrun on an ABI 7900 (Life Technologies Corporation, Carlsbad, Calif.). Ctvalues for each miR assay were normalized to the Ct values ofinter-plate calibrator (IPC) probes and RT-PCR controls. Several qualitychecks were put into place. Samples were eliminated from analysis whenIPC Ct values were >25, RT-PCR Cr values were >35 and when samples didnot amplify control miRs (i.e., miR-16 and miR-21). Principal componentanalysis of the sample data was performed using GeneSpring software(Agilent Technologies, Inc., Santa Clara, Calif.) to identify outliers.Three samples were eliminated from the analysis due for failing toqualify using these quality measures.

Data was subjected to a paired t-test between sample groups as specifiedbelow and p-values were corrected with a Benjamini and Hochbergfalse-discovery rate test. miRs showing the most significant p-valueswere validated using a Taqman probe approach.

The 84 prostate cancers and 28 controls were compared. Eighty-one (81)of 750 miR probes had a >2.0 fold change in level between the PCa andcontrol samples (six down and 75 up). Of those 81, ten had a correctedp-value of <0.05. See Table 32.

TABLE 32 miR expression in PCa versus control samples Regulation inMicroRNA p-value PCa samples Fold Change hsa-miR-574-3p 0.0339 up 3.38hsa-miR-141 0.0339 up 4.26 hsa-miR-331-3p 0.0442 up 5.53 hsa-miR-4320.0442 up 3.32 hsa-miR-326 0.0339 up 5.10 hsa-miR-2110 0.0339 up 6.71hsa-miR-107 0.0317 up 11.31 hsa-miR-130b 0.0339 up 4.66 hsa-miR-301a0.0442 up 5.21 hsa-miR-625* 0.0442 up 3.55

A comparison as above was repeated without 16 M1 metastatic samples.This comparison identified 81 of 750 miRs with >2.0 fold change in levelbetween the PCa and control samples. See Table 33. “Regulation incontrols” refers to the upregulation (up) or downregulation (down) incontrol versus PCa samples.

TABLE 33 miR expression in PCa (no metastatic samples) versus controlsamples MicroRNA Fold change Regulation in controls hsa-miR-107 12.78down hsa-miR-2110 6.42 down hsa-miR-326 5.75 down hsa-miR-301a 4.87 downhsa-miR-185 4.41 down hsa-miR-331-3p 4.28 down hsa-miR-373* 4.11 downhsa-miR-99a 4.06 down hsa-miR-432 3.96 down hsa-miR-625* 3.83 downhsa-miR-130b 3.60 down hsa-miR-638 3.56 down hsa-miR-425* 3.55 downhsa-miR-627 3.53 down hsa-miR-197 3.46 down hsa-miR-532-3p 3.45 downhsa-miR-124 3.42 down hsa-miR-411* 3.42 down hsa-miR-154 3.40 downhsa-miR-16-2* 3.24 down hsa-miR-574-3p 3.23 down hsa-miR-421 3.21 downhsa-miR-18a 3.17 down hsa-miR-141 3.12 down hsa-miR-423-3p 3.09 downhsa-miR-103 3.05 down hsa-miR-28-3p 3.01 down hsa-miR-375 3.00 downhsa-miR-765 2.77 down hsa-miR-362-5p 2.77 down hsa-miR-22* 2.75 downhsa-miR-181b 2.74 down hsa-miR-186 2.74 down hsa-miR-652 2.69 downhsa-miR-192 2.61 up hsa-miR-518f* 2.61 down hsa-miR-1207-5p 2.59 downhsa-miR-532-5p 2.58 down hsa-miR-484 2.56 down hsa-miR-577 2.51 uphsa-miR-379* 2.51 down hsa-miR-363 2.51 down hsa-miR-1224-3p 2.49 downhsa-miR-210 2.46 down hsa-miR-181a-2* 2.44 up hsa-miR-19b 2.41 downhsa-miR-604 2.40 down hsa-miR-125a-5p 2.39 up hsa-miR-1 2.38 downhsa-miR-518c* 2.36 down hsa-miR-95 2.33 down hsa-miR-140-5p 2.33 downhsa-miR-497 2.31 down hsa-miR-491-5p 2.28 down hsa-miR-144 2.26 downhsa-miR-18b 2.25 down hsa-miR-423-5p 2.25 down hsa-miR-665 2.25 downhsa-miR-324-3p 2.25 down hsa-miR-335 2.24 down hsa-miR-590-5p 2.20 downhsa-miR-130a 2.19 down hsa-miR-133b 2.18 down hsa-miR-1972 2.16 downhsa-miR-744* 2.15 down hsa-miR-202 2.14 up hsa-miR-30e 2.12 downhsa-miR-214 2.10 down hsa-miR-29c 2.10 down hsa-miR-20a 2.10 downhsa-miR-1247 2.09 up hsa-miR-15b* 2.08 down hsa-miR-133a 2.08 downhsa-miR-194 2.04 down hsa-miR-26b* 2.04 down hsa-miR-191 2.04 downhsa-miR-106b 2.03 down hsa-miR-485-3p 2.03 down hsa-miR-1909 2.02 downhsa-miR-628-5p 2.02 up hsa-miR-431 2.00 down

Of those 81 miRs in Table 33, nine had an uncorrected p-value <0.01. Allwere upregulated in PCa. See Table 34. “Regulation” refers to theupregulation (up) or downregulation (down) in PCa versus controlsamples.

TABLE 34 miR expression in PCa (no metastatic samples) versus controlsamples miR p-value Regulation Fold Change hsa-miR-107 0.0002 up 12.78hsa-miR-326 0.0015 up 5.75 hsa-miR-432 0.0024 up 3.96 hsa-miR-574-3p0.0029 up 3.23 hsa-miR-625* 0.0038 up 3.83 hsa-miR-2110 0.0044 up 6.42hsa-miR-301a 0.0079 up 4.87 hsa-miR-141 0.0087 up 3.12 hsa-miR-373*0.0090 up 4.11

In further validation of the above results, microRNA was extracted frommicrovesicles extracted from the plasma of 35 biopsy confirmed controlmen (no PCa) and 133 men with non-metastatic prostate cancer. ThemicroRNA was evaluated for the expression of miR-107 and miR-574-3pusing an ABI Taqman assay and was absolutely quantified for copy numberusing a synthetic standard curve. Samples were normalized for RNAisolation variation using 75 femptomol of C. elegans miR-39 as aspike-in during the RNA isolation. Results from each group were comparedusing a Mann-Whitney U test. Results are shown in FIG. 102A (miR-107)and FIG. 102B (miR-574-3p). The p-values were significantly differentbetween controls and PCa samples, as indicated below each figure,thereby validating the results obtained with the Exiqon cards as shownin Tables 32-34. miR-141 was also validated with Taqman in similarexperiments when comparing control and PCa samples.

A comparison as above was repeated between the 16 M1 metastatic PCasamples and 55 M0 non-metastatic PCa samples. This comparison identified121 of 750 miRs with >2.0 fold change in level between the metastaticand non-metastatic samples. See Table 35. “Regulation in non-metastatic”refers to the upregulation (up) or downregulation (down) innon-metastatic versus metastatic PCa samples.

TABLE 35 miR expression in M1 metastatic PCa versus M0 non-metastaticPCa Fold change Regulation in Detector ([M0] vs [M1]) non-metastatichsa-miR-375 12.75 down hsa-miR-497 5.90 down hsa-miR-572 5.89 uphsa-miR-17* 5.65 down hsa-miR-197 5.45 down hsa-miR-141 5.23 downhsa-miR-148a 5.19 down hsa-miR-624* 4.93 down hsa-miR-577 4.88 downhsa-miR-32 4.82 up hsa-miR-200b 4.75 down hsa-miR-130b 4.70 downhsa-miR-210 4.60 down hsa-miR-185 4.55 up hsa-miR-451 4.51 uphsa-miR-146b-5p 4.42 down hsa-miR-452 4.17 down hsa-miR-342-3p 4.12 uphsa-miR-382 4.11 down hsa-miR-331-3p 4.10 down hsa-miR-100 3.97 downhsa-miR-181a 3.77 down hsa-miR-532-3p 3.74 down hsa-miR-636 3.62 downhsa-miR-1296 3.60 down hsa-miR-185* 3.43 up hsa-miR-1 3.41 downhsa-miR-145 3.39 down hsa-miR-10a 3.35 down hsa-miR-30b 3.30 uphsa-miR-199a-5p 3.24 down hsa-miR-30a 3.23 down hsa-miR-425 3.20 downhsa-miR-1972 3.20 down hsa-miR-20a* 3.19 down hsa-miR-142-5p 3.18 downhsa-miR-361-3p 3.17 down hsa-miR-195 3.12 down hsa-miR-1471 3.08 downhsa-miR-143 3.08 down hsa-miR-218-1* 3.08 down hsa-miR-1247 3.07 downhsa-miR-450b-3p 3.05 down hsa-miR-619 2.94 down hsa-miR-339-3p 2.90 downhsa-miR-555 2.88 down hsa-miR-629 2.87 down hsa-miR-26b 2.87 downhsa-miR-373* 2.85 up hsa-miRPlus-A1031 2.84 down hsa-miR-99b* 2.82 downhsa-miR-323-3p 2.80 down hsa-miR-486-5p 2.80 up hsa-miR-1539 2.79 uphsa-miR-17 2.79 down hsa-miR-27b 2.79 up hsa-miR-133a 2.78 downhsa-miR-144 2.76 up hsa-miR-885-5p 2.64 down hsa-let-7c* 2.64 downhsa-miR-10b 2.62 down hsa-miR-454 2.62 down hsa-miR-708* 2.59 downhsa-miR-140-5p 2.59 down hsa-miR-520h 2.58 down hsa-miR-16 2.57 uphsa-miR-886-5p 2.53 down hsa-let-7a 2.53 up hsa-miR-1913 2.50 downhsa-miR-363 2.49 down hsa-miR-517c 2.49 down hsa-miR-130a 2.46 downhsa-miR-192 2.45 down hsa-miR-132* 2.45 down hsa-miR-432 2.41 uphsa-miR-410 2.40 down hsa-miR-22 2.39 up hsa-miR-122* 2.39 downhsa-miR-937 2.39 up hsa-miR-1236 2.37 down hsa-miR-411* 2.36 uphsa-miR-128 2.35 down hsa-miR-340 2.34 down hsa-miR-152 2.31 downhsa-miR-126 2.31 up hsa-miR-30a* 2.30 down hsa-miR-186 2.30 uphsa-miR-590-5p 2.30 down hsa-miR-2110 2.30 down hsa-miR-139-5p 2.29 uphsa-miR-543 2.29 down hsa-miR-181c 2.27 down hsa-miR-582-3p 2.26 downhsa-miR-199a-3p 2.23 down hsa-miR-320b 2.22 up hsa-miR-182* 2.21 downhsa-miR-485-3p 2.19 down hsa-miR-30e 2.19 up hsa-miR-429 2.17 downhsa-miR-628-5p 2.17 down hsa-miR-142-3p 2.16 up hsa-miR-505* 2.16 downhsa-miR-628-3p 2.16 down hsa-miR-300 2.15 up hsa-miR-503 2.15 downhsa-miR-662 2.14 down hsa-miR-548c-5p 2.13 down hsa-miR-1979 2.12 uphsa-miR-433 2.11 down hsa-miR-609 2.10 down hsa-miR-18a 2.10 downhsa-miR-23a* 2.09 down hsa-miR-193a-5p 2.08 down hsa-miR-103-2* 2.08down hsa-miR-622 2.08 up hsa-miR-320a 2.07 up hsa-miR-9 2.07 downhsa-miR-15b* 2.04 down hsa-miR-194 2.03 down hsa-miR-513a-5p 2.03 downhsa-miR-631 2.01 down

Of those 121 miRs in Table 35, nine had a p-value <0.01. See Table 36.All nine were upregulated in metastatic PCa samples.

TABLE 36 miR expression in M1 metastatic PCa versus M0 non-metastaticPCa Regulation miR p-value in metastatic Fold Change hsa-miR-200b 0.0007up 4.75 hsa-miR-375 0.0011 up 12.75 hsa-miR-582-3p 0.0020 up 2.26hsa-miR-17* 0.0023 up 5.65 hsa-miR-1296 0.0034 up 3.60 hsa-miR-20a*0.0039 up 3.19 hsa-miR-100 0.0061 up 3.97 hsa-miR-452 0.0091 up 4.17hsa-miR-577 0.0098 up 4.88 miR-17* and miR-20a* are located in theoncomir1 cluster.

Taqman assays were used to validate several miRs in the metastaticversus non-metastatic PCa setting. FIGS. 103A-D illustrate levels ofmiR-141 (FIG. 103A), miR-375 (FIG. 103B), miR-200b (FIG. 103C) andmiR-574-3p (FIG. 103D) in vesicles isolated from metastatic (M1) andnon-metastatic (M0) prostate cancer samples. In all cases, the p-valueswere significant when comparing miR levels between the metastatic andnon-metastatic samples.

A summary of the Taqman validation results is shown in Table 37. In thetable, M0 and M1 refer to sample numbers used to test each microRNA.P-values were significant in all cases.

TABLE 37 miR expression in M1 metastatic PCa versus M0 non-metastaticPCa by Taqman miR M0 M1 p-value hsa-miR-200b 73 33 0.05 hsa-miR-375 7133 0.0001 hsa-miR-141 73 39 0.0001 hsa-miR-331-3p 64 27 0.002hsa-miR-181a 65 27 0.002 hsa-miR-574-3p 65 32 0.0001

Levels of hsa-miR-141 and hsa-miR-375 from the RNA of serum-derivedvesicles in a separate cohort of 47 metastatic prostate cancer patientswas also found to be significantly higher than in 72 non-recurringprostate cancer patients (p=0.0001).

Vesicles from plasma and serum are reliable sources of microRNA forbiomarkers. Two miRs, hsa-miR-107 and 574-3p were found particularlyelevated in prostate cancer samples compared to biopsy confirmedcontrols. In metastatic plasma-derived vesicle samples, several miRswere found to be significantly elevated, and 2 of these, hsa-miR-141 andhsa-miR-375, were also found to be particularly elevated in metastaticserum-derived vesicles.

Example 42 miRs to Enhance Vesicle Diagnostic Assay Performance

As described herein, vesicles are concentrated in plasma patient samplesand assessed to provide a diagnostic, prognostic or theranostic readout.Vesicle analysis of patient samples includes the detection of vesiclesurface biomarkers, e.g., surface antigens, and/or vesicle payload,e.g., mRNAs and microRNAs, as described herein. The payload within thevesicles can be assessed to enhance assay performance. For example, FIG.104A illustrates a scheme for using miR analysis within vesicles toconvert false negatives into true positives, thereby improvingsensitivity. In this scheme, samples called negative by the vesiclesurface antigen analysis are further confirmed as true negatives or truepositives by assessing payload with the vesicles. Similarly, FIG. 104Billustrates a scheme for using miR analysis within vesicles to convertfalse positives into true negatives, thereby improving specificity. Inthis scheme, samples called positive by the vesicle surface antigenanalysis are further confirmed as true negatives or true positives byassessing payload with the vesicles.

A diagnostic test for prostate cancer includes isolating vesicles from ablood sample from a patient to detect vesicles indicative of thepresence or absence of prostate cancer. See, e.g., Examples 27-34. Theblood can be serum or plasma. The vesicles are isolated by capture with“capture antibodies” that recognize specific vesicle surface antigens.The surface antigens for the prostate cancer diagnostic assay includethe tetraspanins CD9, CD63 and CD81, which are generally present onvesicles in the blood and therefore act as general vesicle biomarkers,the prostate specific biomarkers PSMA and PCSA, and the cancer specificbiomarker B7H3. The capture antibodies are tethered to fluorescentlylabeled beads, wherein the beads are differentially labeled for eachcapture antibody. Captured vesicles are further highlighted usingfluorescently labeled “detection antibodies” to the tetraspanins CD9,CD63 and CD81. Fluorescence from the beads and the detection antibodiesis used to determine an amount of vesicles in the plasma sampleexpressing the surface antigens for the prostate cancer diagnosticassay. The fluorescence levels in a sample are compared to a referencelevel that can distinguish samples having prostate cancer. In thisExample, microRNA analysis is used to enhance the performance of thevesicle-based prostate cancer diagnostic assay.

FIG. 104C shows the results of detection of miR-107 in samples assessedby the vesicle-based prostate cancer diagnostic assay. FIG. 104D showsthe results of detection of miR-141 in samples assessed by thevesicle-based prostate cancer diagnostic assay. In the figure,normalized levels of the indicated miRs are shown on the Y axis for truepositives (TP) called by the vesicle diagnostic assay, true negatives(TN) called by the vesicle diagnostic assay, false positives (FP) calledby the vesicle diagnostic assay, and false negatives (FN) called by thevesicle diagnostic assay. As shown in FIG. 104C, the use of miR-107enhances the sensitivity of the vesicle assay by distinguishing falsenegatives from true negative (p=0.0008) Similarly, FIG. 104D also showsthat the use of miR-141 enhances the sensitivity of the vesicle assay bydistinguishing false negatives from true negative (p=0.0001). Results ofadding miR-141 are shown in Table 38. miR-574-3p performs similarly.

TABLE 38 Addition of miR-141 to vesicle-based test for PCa WithoutmiR-141 With miR-141 Sensitivity 85% 98% Specificity 86% 86%

In this Example, vesicles are detected via surface antigens that areindicative of prostate cancer, and the performance of the signature isfurther bolstered by examining miRs within the vesicles, i.e.,sensitivity is increased without negatively affecting specificity. Thisgeneral methodology can be extended for any setting in which vesiclesare profiled for surface antigens or other informative characteristic,then one or more additional biomarker is used to enhancecharacterization. Here, the one or more additional biomarkers are miRs.They could also comprise mRNA, soluble protein, lipids, carbohydratesand any other vesicle-associated biological entities that are useful forcharacterizing the phenotype of interest.

Example 43 Comparison of miR Expression Patterns in Plasma, Serum andCell Line Vesicles

Agilent v3 miRNA microarrays (Agilent Technologies, Inc., Santa Clara,Calif.) were used to compare expression of vesicle-derived microRNA(miR) between plasma and serum from patients with prostate cancer,healthy controls, one prostate cancer cell line, and prostate tumor andnormal tissue. Total RNA was isolated from plasma and cell line vesiclesusing the Qiagen miReasy kit and from serum using the Exomir extractionmethod (Bioo Scientific Corp., Austin, Tex.). 100 ng of each sample washybridized to the microarrays and the extracted data was analyzed withthe GeneSpring software package. Hierarchal clustering on both samplesand genes demonstrated a distinct expression pattern of plasma vesiclescompared to cell-line derived vesicles and tumor tissue. Serum andplasma from prostate cancer patients and normal controls were evaluatedfor significantly differentially expressed microRNAs. Vesicles derivedfrom peripheral blood offer a unique source for blood-based miRanalysis.

Example 44 Isolating Subpopulations of Vesicles and Subsequent miRProfiles

In this Example, microRNA (miR) expression patterns were examined incirculating microvesicle subpopulations that were defined based onsurface protein composition. Vesicles isolated from a prostate cancercell line (VCaP) were flow sorted based on their surface proteincomposition using methodology as described herein. The vesicles wereevaluated for differential expression of miRs. Phycoerythrin-labeledantibodies targeting EpCam, CD63, or B7-H3 were used to sort thesubpopulations of vesicles by fluorescence-activated cell sorting.Vesicles were sorted on a Beckman-Coulter MoFlo XDP (Beckman Coulter,Inc., Brea, Calif.) so that each vesicle could be analyzed as anindividual particle. There was a significant shift in the intensity ofthe FL2 channel over the isotype control due to the abundance of theantigen on the surface of the vesicles. The sorted subpopulations ofvesicles were subsequently profiled by miR expression. The miR profilesfor the EpCam, CD63, and B7-H3 positive subpopulations were compared tothe profile of the total VCaP vesicle population. Differential miRexpression patterns were observed across the subpopulations and allexpression patterns were distinct from that observed in the totalpopulation. Patterns of both over- and under-expression of miRs wereobserved between groups. These data show that subpopulations of vesiclescan be distinguished and separated based on surface protein markers aswell as their genetic content, in this case miRs. The ability to isolatetissue-specific vesicle populations from patient plasma based on surfaceprotein composition and then analyze them based on both surface proteincomposition and genetic content can be used for diagnostic, prognostic,and theranostic applications as described herein.

Example 45 MicroRNA Biomarkers in Men with Prostate Cancer and Low PSA

Although an enlarged prostate combined with low PSA, e.g., less than 4ng/ml, may indicate benign prostate hyperplasia (BPH), these clinicalobservations are indicative of prostate cancer rather than BPH in somecases. A biomarker that can distinguish between these two groups wouldallow for the early detection of prostate cancer in symptomatic men withlow PSA.

Using methodology described herein, vesicles were isolated from plasmasamples from 13 control patients with PSA <4.0 ng/ml (group 1), 15control patients with PSA ≧4.0 ng/ml (group 2), nine non-metastaticprostate cancer patients with PSA <4.0 ng/ml (group 3) and 59non-metastatic prostate cancer patients with PSA ≧4.0 ng/ml (group 4).MicroRNA payload was isolated from the vesicles and was examined usingthe Exiqon RT-PCR panel consisting of 750 miR probes as describedherein. Normalized results were compared between prostate cancerpatients with PSA >4.0 (group 4) and prostate cancer patients with PSA<4.0 (group 3). 344 miR probes were found to have a fold change greaterthan two-fold between these groups. See Table 39. In Table 39, “Foldchange” refers to the change in levels between groups 3 and 4 and“Regulation” refers to upregulation (up) or downregulation (down) ingroup 4 as compared to group 3.

TABLE 39 Fold change in miR levels in PCa samples with PSA < or ≧ 4.0ng/ml MicroRNA Fold change Regulation hsa-miR-143 41.44 down hsa-miR-43231.23 down hsa-miR-425 18.66 down hsa-miR-32 17.55 down hsa-miR-42415.78 down hsa-miR-96 14.72 down hsa-miR-629 14.54 down hsa-miR-532-5p13.43 down hsa-miR-215 13.40 down hsa-miR-920 12.21 down hsa-miR-42111.78 down hsa-miR-204 11.18 down hsa-miR-29a 11.04 down hsa-miR-148a10.62 down hsa-miR-19b 10.00 down hsa-miR-595 9.96 down hsa-miR-590-5p9.93 down hsa-miR-518f 9.76 down hsa-miR-518f* 9.68 down hsa-miR-7669.49 down hsa-miR-22* 9.01 down hsa-miR-491-5p 8.95 down hsa-miR-29b8.83 down hsa-miR-144 8.52 down hsa-miR-451 8.33 down hsa-miR-376a 8.27down hsa-miR-577 8.22 down hsa-miR-151-3p 8.18 down hsa-let-7f 7.74 downhsa-miR-188-3p 7.52 up hsa-let-7i 7.52 down hsa-miR-19a 7.45 downhsa-miR-616* 7.35 down hsa-miR-140-3p 7.28 down hsa-miR-18a* 7.24 downhsa-miR-154* 7.11 down hsa-miR-423-5p 7.11 down hsa-miR-192 7.01 downhsa-miR-212 7.00 down hsa-miR-107 6.95 down hsa-miR-16-2* 6.89 downhsa-miR-205 6.84 down hsa-miR-199a-3p 6.84 down hsa-miR-101 6.78 downhsa-miR-130a 6.75 down hsa-miR-15a 6.64 down hsa-miR-363 6.58 downhsa-miR-30b* 6.56 down hsa-miR-146b-5p 6.54 down hsa-miR-142-5p 6.48down hsa-miR-197 6.44 down hsa-miR-339-5p 6.41 down hsa-miR-140-5p 6.39down hsa-miR-450a 6.27 down hsa-miR-624* 6.21 down hsa-miR-122 6.15 downhsa-miR-665 6.13 down hsa-miR-125b 6.06 down hsa-miR-937 6.05 downhsa-miR-148b 5.99 down hsa-miR-106b* 5.99 down hsa-miR-769-5p 5.95 downhsa-miR-1255b 5.94 down hsa-miR-517* 5.87 down hsa-miR-517a 5.85 downhsa-let-7d 5.68 down hsa-miR-365* 5.67 down hsa-miR-302d* 5.57 downhsa-miR-221* 5.54 down hsa-miR-103-2* 5.47 down hsa-miR-136 5.43 downhsa-let-7g 5.43 down hsa-miR-424* 5.25 down hsa-miR-124 5.12 downhsa-miR-103 5.10 down hsa-miR-23b* 5.09 down hsa-miR-191 5.08 downhsa-miR-221 5.06 down hsa-miR-324-5p 5.06 down hsa-miR-330-5p 5.03 downhsa-miR-302b 5.01 down hsa-miR-570 4.96 down hsa-miR-105* 4.95 downhsa-miR-15b 4.95 down hsa-miR-29c 4.91 down hsa-miR-497 4.76 downhsa-miR-617 4.74 down hsa-miR-1200 4.69 down hsa-miR-29a* 4.62 downhsa-miR-1468 4.62 down hsa-miR-24 4.58 down hsa-miR-181a* 4.56 downhsa-miR-211 4.56 down hsa-let-7b 4.55 down hsa-miR-103-as 4.50 downhsa-miR-302a 4.45 down hsa-miR-30b 4.45 down hsa-miR-765 4.44 downhsa-miRPlus-A1031 4.43 down hsa-miR-181a 4.42 down hsa-miR-25 4.38 downhsa-miR-324-3p 4.37 down hsa-miR-199a-5p 4.32 down hsa-miR-375 4.32 downhsa-miR-887 4.30 down hsa-miR-1179 4.22 down hsa-miR-27a 4.20 downhsa-miR-411* 4.19 down hsa-miR-10a 4.19 down hsa-miR-609 4.18 downhsa-miR-342-3p 4.18 down hsa-miR-219-2-3p 4.16 down hsa-miR-299-5p 4.14down hsa-miR-1 4.14 down hsa-miR-23a* 4.05 down hsa-miR-31 4.04 downhsa-miR-1260 4.01 down hsa-miR-335 3.99 down hsa-miR-93 3.98 downhsa-miR-148b* 3.93 down hsa-miR-376b 3.91 down hsa-miR-376c 3.90 downhsa-miR-16 3.89 down hsa-miR-30c 3.88 down hsa-miR-21* 3.87 downhsa-miR-185* 3.87 down hsa-miR-139-5p 3.83 down hsa-miR-331-3p 3.82 downhsa-miR-210 3.80 down hsa-miR-371-3p 3.80 down hsa-miR-328 3.79 downhsa-miR-886-5p 3.77 up hsa-let-7c* 3.77 down hsa-miR-484 3.74 downhsa-miR-198 3.72 down hsa-miR-584 3.72 down hsa-miR-99b* 3.71 downhsa-miR-619 3.69 down hsa-miR-654-3p 3.66 down hsa-miR-377 3.65 downhsa-miR-636 3.64 down hsa-miR-921 3.63 down hsa-miR-518e* 3.59 downSNORD49A 3.56 down hsa-miR-188-5p 3.54 down hsa-miR-532-3p 3.52 downhsa-miR-1266 3.50 down hsa-miR-410 3.50 down hsa-miR-34b* 3.50 uphsa-miR-505* 3.49 down hsa-miR-18a 3.49 down hsa-miR-297 3.43 downhsa-miR-940 3.43 down hsa-miR-582-3p 3.43 down hsa-miR-7-2* 3.43 downhsa-miR-30e 3.42 down hsa-miRPlus-A1027 3.42 down hsa-miR-146a 3.39 downhsa-miR-21 3.38 down hsa-miR-431 3.38 down hsa-miR-495 3.38 downhsa-miR-106a 3.37 down hsa-miR-574-3p 3.37 down hsa-miR-526b 3.37 downhsa-miR-651 3.37 down hsa-miR-92a 3.37 down hsa-miR-182 3.36 downhsa-miR-631 3.34 down hsa-miR-675* 3.34 down hsa-miR-374b* 3.34 downhsa-miR-300 3.31 down hsa-miRPlus-C1070 3.31 down hsa-miR-135a 3.31 downhsa-miR-449a 3.27 down hsa-miR-187 3.23 down hsa-miR-19b-1* 3.21 downhsa-miR-412 3.19 down hsa-miR-345 3.18 down hsa-miR-202 3.14 downhsa-miR-524-5p 3.14 down hsa-miR-10b 3.14 down hsa-miR-452 3.14 downhsa-miR-141 3.13 down hsa-miR-217 3.11 down hsa-miR-17* 3.10 downhsa-miR-200a 3.10 down hsa-miR-523 3.08 down hsa-miR-642 3.07 downhsa-miR-378 3.05 down hsa-miR-99b 3.04 down hsa-miR-339-3p 3.02 downhsa-miR-942 3.01 down hsa-miR-555 2.99 down hsa-miR-222 2.99 downhsa-miR-151-5p 2.98 down hsa-miR-634 2.97 down hsa-miR-628-5p 2.96 downhsa-miR-223 2.95 down hsa-miR-486-5p 2.95 down hsa-miR-142-3p 2.93 downhsa-miR-130b 2.92 down hsa-miR-220b 2.92 down hsa-miR-218 2.90 downhsa-miR-132 2.89 down hsa-miR-20a 2.89 down hsa-miR-320a 2.88 downhsa-miR-553 2.87 down hsa-miR-27b 2.87 down hsa-miR-620 2.86 downhsa-miR-28-5p 2.84 down hsa-miR-1913 2.83 down hsa-miR-150 2.83 downhsa-miR-301b 2.83 down hsa-miR-520d-3p 2.82 up hsa-miR-126 2.82 downhsa-miR-654-5p 2.81 down hsa-miR-558 2.81 down hsa-miR-586 2.80 downhsa-miR-516b 2.77 down hsa-miR-1269 2.77 down hsa-miR-658 2.76 downhsa-miR-92a-1* 2.75 down hsa-miR-92a-2* 2.75 down hsa-miR-625* 2.74 downhsa-miR-1205 2.74 down hsa-miR-224* 2.74 down hsa-miR-326 2.72 downhsa-miR-573 2.72 down hsa-miR-1909 2.72 down hsa-miR-500 2.71 downhsa-miR-7 2.71 down hsa-miR-583 2.70 down hsa-miR-185 2.69 downhsa-miR-943 2.69 down hsa-miR-544 2.69 down hsa-miR-9 2.68 downhsa-miR-22 2.67 down hsa-miR-1252 2.67 down hsa-miR-876-3p 2.64 downhsa-miR-890 2.64 down hsa-miR-520c-3p 2.63 down hsa-miR-1270 2.63 downhsa-miR-296-3p 2.62 down hsa-miR-450b-3p 2.62 down hsa-miR-200b 2.62down hsa-miR-576-5p 2.61 down hsa-miR-767-5p 2.61 down hsa-miR-888 2.61down hsa-miR-216a 2.60 down hsa-miRPlus-C1089 2.60 down hsa-miR-340*2.60 down hsa-miR-214* 2.59 down hsa-miR-550 2.59 down hsa-miR-510 2.58down hsa-miR-34c-3p 2.57 down hsa-miR-135b 2.57 down hsa-miR-106b 2.56down hsa-miR-512-3p 2.56 down hsa-miR-1237 2.56 down hsa-miR-543 2.56 uphsa-miR-18b 2.54 down hsa-miR-125a-5p 2.53 down hsa-miR-135b* 2.53 downhsa-miR-760 2.52 up hsa-miR-184 2.51 down hsa-miR-629* 2.47 downhsa-miR-1238 2.47 down hsa-miR-138 2.47 down hsa-miR-365 2.46 downhsa-let-7g* 2.45 down hsa-miR-744 2.45 down hsa-miR-133a 2.45 downhsa-miR-557 2.44 down hsa-miR-454* 2.44 down hsa-miR-26b* 2.44 downhsa-miR-593* 2.43 down hsa-miR-548c-5p 2.42 down hsa-miR-653 2.41 downhsa-miR-708 2.41 down hsa-miR-15a* 2.41 down hsa-miR-452* 2.40 downhsa-miR-186 2.40 down hsa-miR-1972 2.39 down hsa-miR-101* 2.39 downhsa-miR-148a* 2.38 down hsa-miR-548a-5p 2.37 down hsa-miR-98 2.36 downhsa-miR-33a* 2.36 down hsa-miR-877* 2.36 down hsa-miRPlus-D1061 2.35down hsa-miR-17 2.35 down hsa-miR-608 2.34 down hsa-miR-92b* 2.34 downhsa-miR-154 2.33 down hsa-miR-27b* 2.33 down hsa-miR-93* 2.33 downhsa-miR-203 2.32 down hsa-miR-603 2.30 up hsa-miR-30d* 2.29 downhsa-miR-373 2.28 down hsa-let-7f-1* 2.28 down hsa-miR-541* 2.27 downhsa-miR-187* 2.27 down hsa-miR-1265 2.26 down hsa-miR-23a 2.25 downhsa-miR-30c-1* 2.25 down hsa-miR-362-5p 2.25 down hsa-miR-30a* 2.25 downhsa-miR-200b* 2.25 down hsa-miR-744* 2.24 down hsa-miR-1979 2.23 downhsa-let-7b* 2.23 down hsa-miR-132* 2.23 down hsa-miR-571 2.22 downhsa-miR-425* 2.20 down hsa-miR-194* 2.20 down hsa-miR-145* 2.17 downhsa-miR-551b 2.17 down hsa-miR-720 2.16 down hsa-miR-302d 2.16 downhsa-miR-195 2.16 down hsa-miR-194 2.16 down hsa-miR-885-3p 2.16 downhsa-miR-579 2.15 down hsa-miR-361-3p 2.15 down hsa-miR-542-5p 2.15 downhsa-miR-320b 2.13 down hsa-miR-155 2.13 up hsa-miR-548j 2.10 downhsa-miR-616 2.10 down hsa-miR-502-5p 2.10 down hsa-miR-662 2.09 downhsa-miR-137 2.08 down hsa-miR-218-1* 2.08 down hsa-miR-1537 2.07 downhsa-miR-143* 2.07 down hsa-miR-1227 2.06 down hsa-miR-23b 2.05 downhsa-miR-675b 2.03 down hsa-miR-323-3p 2.03 down hsa-miR-889 2.02 downhsa-miR-485-3p 2.02 up hsa-miR-545 2.01 up hsa-miR-340 2.00 down

An unpaired t-test with a Benjamini and Hochberg false discovery rate(FDR) of <0.05 was performed on the microRNAs in Table 39. See Benjaminiand Hochberg. “Controlling the false discovery rate: a practical andpowerful approach to multiple testing” Journal of the Royal StatisticalSociety, Series B (Methodological) 57: 289-300 (1995). 32 significantprobes were identified that met these criteria. See Table 40. In Table40, corrected p-values are shown and regulation refers to theupregulation (up) or downregulation (down) in group 3 as compared togroup 4.

TABLE 40 miR levels in miR levels in PCa samples with PSA < or ≧ 4.0ng/ml MicroRNA p-value Regulation Fold Change hsa-miR-432 0.0025 up31.23 hsa-miR-23b* 0.0073 up 5.09 hsa-miR-518f 0.0073 up 9.76 hsa-miR-960.0073 up 14.72 hsa-miR-154* 0.0084 up 7.11 hsa-miR-143 0.0157 up 41.44hsa-miR-424* 0.0157 up 5.25 hsa-miR-219-2-3p 0.0257 up 4.16 hsa-miR-517a0.0313 up 5.85 hsa-let-7b 0.0313 up 4.55 hsa-miR-450a 0.0344 up 6.27hsa-miR-204 0.0415 up 11.18 hsa-miR-19b-1* 0.0415 up 3.21 hsa-miR-2170.0441 up 3.11 hsa-miR-181a* 0.0441 up 4.56 hsa-miR-150 0.0441 up 2.83hsa-miR-629 0.0442 up 14.54 hsa-miR-148b* 0.0442 up 3.93 hsa-miR-6170.0442 up 4.74 hsa-miR-18a* 0.0442 up 7.24 hsa-miR-517* 0.0442 up 5.87hsa-miR-451 0.0442 up 8.33 hsa-miR-595 0.0442 up 9.96 hsa-miR-634 0.0442up 2.97 hsa-miR-93 0.0442 up 3.98 hsa-miR-1270 0.0442 up 2.63hsa-miR-424 0.0442 up 15.78 hsa-miR-299-5p 0.0442 up 4.14 hsa-miR-365*0.0442 up 5.67 hsa-miR-215 0.0442 up 13.40 hsa-miR-769-5p 0.0442 up 5.95hsa-miR-1205 0.0442 up 2.74

The set of 32 probes in Table 40 was examined in the context of the fouraforementioned groups 1-4. Six microRNAs were found with expressiondifferences greater than 5-fold and wherein the mean of the prostatecancer PSA <4.0 group did not overlap with the interquartile range ofthe control groups. These miRs can distinguish cancer from no cancer insymptomatic men with PSA <4.0. This selection consisted of the miRsshown in FIGS. 105A-105F: hsa-miR-432 (FIG. 105A), hsa-miR-143 (FIG.105B), hsa-miR-424 (FIG. 105C), hsa-miR-204 (FIG. 105D), hsa-miR-581f(FIG. 105E) and hsa-miR-451 (FIG. 105F). In the figures, the X axisshows the four groups of samples: “Control no” are control patients withPSA ≧4.0 ng/ml (group 2); “Control yes” are control patients with PSA<4.0 (group 1) ng/ml; “Diseased no” are prostate cancer patients withPSA ≧4.0 (group 4) ng/ml; and “Diseased yes” are prostate cancerpatients with PSA <4.0 (group 3) ng/ml.

Example 46 Prostate Cancer-Related microRNAs

FIG. 106 illustrates the levels of microRNAs miR-29a and miR-145 invesicles isolated from plasma samples from prostate cancer (PCa) andcontrols. For miR-29a, data is shown for 81 controls and 130 PCa cases.For miR-145, data is shown for 81 controls and 126 PCa cases. A pairedt-test revealed that the levels of miR-29a (p<0.001) and miR-145(p<0.0001) were significantly different between cases and controls.

Example 47 microRNAs Before and after Treatment for PCa

Fifteen prostate cancer patients had a plasma sample drawn before andafter treatment. The treatment was either radical prostatectomy orradiation therapy. RNA derived from microvesicles of the plasma sampleswas evaluated on the Exiqon microRNA ready to use qRT-PCR panel. SeeExamples 17-18 for further details. Results were normalized tointer-plate calibrator probes and then subjected to a paired t-test.P-values were corrected with a Benjamini and Hochberg false-discoveryrate test. Table 41 shows several statistically significant miRs fromthis comparison of miR expression before and after treatment.Fold-change is the amount increase of these miRs in samples beforetreatment compared to samples after treatment. All miRs in Table 41 wereover-expressed in the before-treatment samples.

TABLE 41 Differentially expressed miRs before and after treatment forPCa Corrected p- miR Fold Change value hsa-miR-1974 12.08 0.0025hsa-miR-27b 7.8 0.0025 hsa-miR-103 10.43 0.0067 hsa-miR-146a 9.24 0.0067hsa-miR-22 4.06 0.0067 hsa-miR-382 8.12 0.0105 hsa-miR-23a 3.93 0.0181hsa-miR-376c 3.51 0.0181 hsa-miR-335 8.26 0.0181 hsa-miR-142-5p 3.850.0202 hsa-miR-221 7.08 0.0245 hsa-miR-142-3p 3.8 0.0302 hsa-miR-151-3p9.1 0.0398 hsa-miR-21 3.81 0.0398

Example 48 Vesicle Isolation and Detection Methods

A number of technologies known to those of skill in the art can be usedfor isolation and detection of vesicles to carry out the methods of theinvention in addition to those described above. The following is anillustrative description of several such methods.

Glass Microbeads.

Available as VeraCode/BeadXpress from Illumina, Inc. San Diego, Calif.,USA. The steps are as follows:

-   -   1. Prepare the beads by direct conjugation of antibodies to        available carboxyl groups.    -   2. Block non specific binding sites on the surface of the beads.    -   3. Add the beads to the vesicle concentrate sample.    -   4. Wash the samples so that unbound vesicles are removed.    -   5. Apply fluorescently labeled antibodies as detection        antibodies which will bind specifically to the vesicles.    -   6. Wash the plate, so that the unbound detection antibodies are        removed.    -   7. Measure the fluorescence of the plate wells to determine the        presence the vesicles.

Enzyme Linked Immunosorbent Assay (ELISA).

Methods of performing ELISA are well known to those of skill in the art.The steps are generally as follows:

-   -   1. Prepare a surface to which a known quantity of capture        antibody is bound.    -   2. Block non specific binding sites on the surface.    -   3. Apply the vesicle sample to the plate.    -   4. Wash the plate, so that unbound vesicles are removed.    -   5. Apply enzyme linked primary antibodies as detection        antibodies which also bind specifically to the vesicles.    -   6. Wash the plate, so that the unbound antibody-enzyme        conjugates are removed.    -   7. Apply a chemical which is converted by the enzyme into a        color, fluorescent or electrochemical signal.    -   8. Measure the absorbency, fluorescence or electrochemical        signal (e.g., current) of the plate wells to determine the        presence and quantity of vesicles.

Electrochemiluminescence Detection Arrays.

Available from Meso Scale Discovery, Gaithersburg, Md., USA:

-   -   1. Prepare plate coating buffer by combining 5 mL buffer of        choice (e.g. PBS, TBS, HEPES) and 75 μL of 1% Triton X-100        (0.015% final).    -   2. Dilute capture antibody to be coated.    -   3. Prepare 5 μL of diluted a capture antibody per well using        plate coating buffer (with Triton).    -   4. Apply 5 μL of diluted capture antibody directly to the center        of the working electrode surface being careful not to breach the        dielectric. The droplet should spread over time to the edge of        the dielectric barrier but not cross it.    -   5. Allow plates to sit uncovered and undisturbed overnight.

The vesicle containing sample and a solution containing the labeleddetection antibody are added to the plate wells. The detection antibodyis an anti-target antibody labeled with an electrochemiluminescentcompound, MSD SULFO-TAG label. Vesicles present in the sample bind thecapture antibody immobilized on the electrode and the labeled detectionantibody binds the target on the vesicle, completing the sandwich. MSDread buffer is added to provide the necessary environment forelectrochemiluminescence detection. The plate is inserted into a readerwherein a voltage is applied to the plate electrodes, which causes thelabel bound to the electrode surface to emit light. The reader detectsthe intensity of the emitted light to provide a quantitative measure ofthe amount of vesicles in the sample.

Nanoparticles.

Multiple sets of gold nanoparticles are prepared with a separateantibody bound to each. The concentrated microvesicles are incubatedwith a single bead type for 4 hours at 37° C. on a glass slide. Ifsufficient quantities of the target are present, there is a colorimetricshift from red to purple. The assay is performed separately for eachtarget. Gold nanoparticles are available from Nanosphere, Inc. ofNorthbrook, Ill., USA.

Nanosight.

A diameter of one or more vesicles can be determined using opticalparticle detection. See U.S. Pat. No. 7,751,053, entitled “OpticalDetection and Analysis of Particles” and issued Jul. 6, 2010; and U.S.Pat. No. 7,399,600, entitled “Optical Detection and Analysis ofParticles” and issued Jul. 15, 2010. The particles can also be labeledand counted so that an amount of distinct vesicles or vesiclepopulations can be assessed in a sample.

Example 49 Protocol for Vesicle Concentration from Plasma

In this example, vesicles are concentrated in plasma patient samples.The protocols can be used for vesicle analysis from patient samples,including the detection of vesicle surface biomarkers, e.g., surfaceantigens, and/or vesicle payload, e.g., mRNAs and microRNAs, asdescribed herein.

Equipment, Reagents & Supplies:

Equipment

-   -   a. Thermo Scientific Sorvall Legend RT Plus Series Benchtop        Centrifuge with 15 ml swinging bucket rotor. Part Number:        75004377 (Thermo Scientific, Part of Thermo Fisher Scientific,        Waltham, Mass.)    -   b. Class II Biosafety Cabinet for plasma handling    -   c. Pipettors: 20 μl, 200 μl, 1000 μl    -   d. Serological pipettor, Pipette Boy, VWR, catalog number:        14222-180 (VWR International, LLC, West Chester, Pa.)    -   e. VWR digital vortex mixer, catalog number: 14005-824    -   f. Computer with interne access

Reagents

-   -   a. 1×PBS, Sigma pH 7.4. catalog number: P-3813 (Sigma, Saint        Louis, Mo., part of Sigma-Aldrich, Inc.)    -   b. Molecular Biology Reagent Water, Sigma, catalog number: W4502

Supplies

-   -   a. 0.8 μm Millex-AA syringe-driven filter unit, Millipore. Part        number: SLAA033SB (Millipore, Billerica, Mass.)    -   b. Pierce concentrators, 150K MWCO (molecular weight cut off)        7 ml. Part number: 89922 (Pierce, Part of Thermo Fisher        Scientific Inc. Rockford, Ill.)    -   c. Non Sterile BD Luer Lock Syringe, 10 ml. Part number: 301029        (BD, Franklin Lakes, N.J.)    -   d. USA Scientific co-polymer 1.5 ml non-binding tubes, USA        Scientific, catalog number 1415-2500 (USA Scientific, Inc.,        Ocala, Fla.)    -   e. 5 ml sterile plugged serological pipettes, Fisher, catalog        number 13-678-11D (Fisher Scientific, Part of Thermo Fisher        Scientific, Pittsburgh, Pa.)    -   f. Ice bucket, Fisher, catalog number 02-591-46    -   g. Tube racks, Fisher, catalog number 05-541-38    -   h. 4-way racks, Fisher, catalog number 03-448-17    -   i. 50 ml conical, VWR, catalog number 21008-951    -   j. Floating tube racks, VWR, catalog number 60986-100    -   k. 1 liter beakers, VWR, catalog number 89000-226    -   l. 10/20 μl filtered pipet tips, Rainin, catalog number GP-L10F        (Rainin Instrument, LLC, Oakland, Calif., a METTLER TOLEDO        Company)    -   m. 200 μl filtered pipet tips, Rainin, catalog number GP-L200F    -   n. 1000 μl filtered pipet tips, Rainin, catalog number GP-L1000F    -   o. Personal protective equipment

Quality Control:

-   -   a. Samples with less than 900 μl volume may provide suboptimal        results and should be avoided.    -   b. Samples that have been through more than one freeze thaw        cycle may provide suboptimal results and should be avoided.    -   c. The 150K MWCO columns can be damaged by pipette tips or        during the manufacture process. The determination of a column        compromise can be assessed by examining the filtrate. If the        filtrate appears to contain a heavy amount of plasma and the        column itself contains a low volume of plasma (<1000) then it is        likely the 7 ml 150K MWCO column has been compromised. If a        column has been suspected of being compromised then the sample        will need to be re-concentrated with another plasma aliquot from        the same patient.

Procedure:

Selecting Samples for Concentration

-   -   a. Enter sample information for the selected samples from the        sample database into a Microsoft Excel spreadsheet (Microsoft        Corp, Redmond, Wash.).    -   b. Print a copy of the Plasma Concentration Bench Sheet from        Excel.

Filter Procedure for Plasma Samples

-   -   a. Fill ice bucket with water from cold tap faucet.    -   b. Find plasma samples listed on the Plasma Concentration Bench        Sheet and remove samples from −80° C. (−65° C. to −85° C.)        freezer. Any remaining cryovials will continue being stored in        the same box at −80° C. (−65° C. to −85° C.) in the event that        an additional aliquot of plasma is required for testing.    -   c. Thaw samples in water drawn in a) by placing inside floating        tube rack. Check plasma after 10 minutes and if all plasma        samples are not completely thawed, leave plasma in water and        check at 5 minute intervals until all plasma samples are thawed.    -   d. During thawing step, remove labels from blue folder and affix        one side label to each 7 ml 150K MWCO columns (1 per plasma        sample) and place in 4-way tube rack. Record lot numbers for        columns on Plasma Concentration Bench Sheet.    -   e. Pour Molecular Biology Reagent water into 1 liter beaker.    -   f. For each sample to be run, fill a 10 ml syringe with 4 mls of        Molecular Biology Reagent water by submerging syringe tip into        water in beaker and drawing up the plunger.    -   g. Attach a 0.8 μm Millipore filter to each syringe tip and pass        contents through the filter onto a 7 ml 150K MWCO column.    -   h. Cap the columns, place in the swing bucket centrifuge and        centrifuge at 1000×g in Sorvall Legend XTR Benchtop centrifuge        for 4 minutes at 20° C. (16° C. to 24° C.).    -   i. While spinning columns, remove Millipore filter from syringe,        pull plunger out of syringe, and replace filter at end of        syringe.    -   j. When centrifuge is done spinning, discard flow through from        the 7 ml 150K MWCO column as well as any residual water left in        the upper filter.    -   k. Place syringe and filter on open 7 ml 150K MWCO column. Fill        open end of syringe with 5.2 ml of 1×PBS prepared in sterile        molecular grade water.    -   l. Using a p1000 pipette, assess and record volume of patient        plasma on Plasma Concentration Bench Sheet.    -   m. If sample is less than 900 μl, a new test on another plasma        aliquot should be performed for that patient. Aquire another        sample for the patient and update the Plasma Concentration Bench        Sheet accordingly.    -   n. Pipette patient plasma (900-1000 μl) into the PBS in the        syringe, pipette mix twice, and discard plasma tube, along with        any remaining patient plasma, and pipette tip into biohazard        waste bin.    -   o. Place the plunger in the syringe and slowly (˜1 ml/second)        depress the plunger until the contents of the syringe have        passed through the filter onto the 7 ml 150K MWCO column.    -   p. Pass entire sample through filter until the 7 ml 150K MWCO        column is full of liquid or bubbles are seen passing through the        filter.    -   q. Discard syringe and attached filter into biohazard waste bin        and tightly cap all 7 ml 150K MWCO columns.

NOTE: Steps k)-q) should be performed inside a biosafety cabinet.

NOTE: If flowthrough from the plasma is not clear and has coloration atany point during concentration, it is likely the column has ruptured andthe sample should be discarded. Similarly, if the concentrated plasmavolume falls below 100 μl at any point during concentration, the sampleshould be discarded. In either case, order a new plasma sample andrepeat this procedure.

Vesicle Concentration Centrifugation Protocol

-   -   a. Centrifuge 7 ml 150K MWCO columns at 2000×g at 20° C. (16° C.        to 24° C.) for 1 hour. Open centrifuge and check samples to see        if they fall within the following plasma concentrate volume        range:        -   Target Volume: 0.3× Original Plasma Volume (in μl)        -   Minimum Allowable Volume: 100 μl        -   For example: if original plasma volume was 900 μl, Target            Volume would be 270 μl (0.3×900=270).    -   b. During 1 hour spin, prepare 100 mls of 10% bleach in 1 liter        beaker.    -   c. During 1 hour spin, affix one side label to each co-polymer        1.5 ml tube (1 per sample).    -   d. After 1 hour spin, pour the flow-through into 10% bleach.        When beaker is full or all samples have been poured off, pour        down drain.    -   e. Visually inspect sample volume. If plasma concentrate is        above the 8.5 ml graduation on the concentrator tube, continue        to spin plasma sample at 10 minute increments at 2000×g at        20° C. (16° C. to 24° C.) checking volume after each spin until        plasma concentrate is between 8.0 and 8.5 mls.    -   f. Avoid scraping the white filter with the pipette tip during        this step. At the conclusion of the spin, with a p1000 pipette        set to 150 pipette mix slowly on the column a minimum of 6 times        (avoid creating bubbles), and adjust pipette to determine plasma        concentrate volume. If volume is between 100 ul and Target        Volume, transfer concentrated plasma to previously labeled        co-polymer 1.5 ml tube. If the volume is still greater than        Target Volume, repeat step e).    -   g. Record concentrated plasma volume on the Plasma Concentration        Bench Sheet and discard concentrator column in biohazard waste        bin.    -   h. Enter the plasma volume, the concentrate volume, concentrator        lot numbers in the electronic Plasma Concentration Bench Sheet,        save, print a new copy of the bench sheet and affix it to the        original copy.    -   i. Pour ˜45 mls of 1×PBS prepared in sterile molecular grade        water into 50 ml conical tube for use in the next step.    -   j. According to the Plasma Concentration Bench Sheet printed        above, add the appropriate amount of 1×PBS to reconstitute the        sample to the Target Volume.    -   k. Store concentrated plasma sample at 4° C. (2° C. to 8° C.)        overnight in tube rack before running analysis on the subsequent        day. Cover rack with plastic lid and label lid with date and        accession numbers.

Calculations:

-   -   a. Final volume of concentrated plasma sample x=y*0.3, where x        is the final volume of concentrate and y is the initial volume        of plasma.        -   Example: Sample volume is 900 μl. 900 μl*0.3=270 μl final            volume.

References:

-   -   a. Pierce concentrators, 150K MWCO (molecular weight cut off)        7 ml. Part number: 89922 Product Insert.

Example 50 Microsphere Vesicle Analysis from Concentrated Plasma

This Example presents a process for evaluating vesicles concentratedpatient plasma samples. The protocols can be used for the analysis ofvesicle surface biomarkers in concentrated plasma samples processed asoutlined in Example 49.

Equipment, Reagents & Supplies:

Equipment

-   -   a. VWR digital vortex mixer, catalog number 14005-824 (VWR        International, LLC, West Chester, Pa.)    -   b. Boekel Scientific Jitterbug 4, catalog number 270440 (Boekel        Scientific, Feasterville, Pa.)    -   c. Pall life sciences vacuum manifold, catalog number 13157        (Pall Corporation, East Hills, N.Y.)    -   d. Pall life sciences multiwall plate vacuum manifold, catalog        number 5017    -   e. Pall life sciences 1 ml receiver plate spacer block, catalog        number 5014    -   f. Pall life sciences waste drain adapter retainer, catalog        number 5028    -   g. Single channel pipettors: 2 μl, 10 μl, 20 μl, 200 μl, 1000 μl    -   h. Eight channel pipettors: 20 μl, 200 μl    -   i. Electronic eight channel pipette: 1000 μl    -   j. Electronic single channel pipette: 200 μl, 1000 μl    -   k. Serological Pipettor, Pipette Boy, VWR, catalog number        14222-180    -   l. Luminex LX200 Instrument (Luminex Corporation, Austin, Tex.)    -   m. Microplate shaker, VWR, catalog number 12620-926    -   n. VWR MiniFuge Microcentrifuge, VWR, catalog number 93000-196    -   o. Ice Machine, Scotsman, catalog number AFE424 (Scotsman Ice        Systems, Vernon Hills, Ill.)

Reagents

-   -   a. NOTE: Antibody reagents listed below are exemplary        antibodies. To perform a test with alternate capture and/or        detection antibodies, antibodies to the biomarkers of interest        are selected as desired.

Exemplary Capture Antibodies—to be chosen depending on desired testobjectives. See Table 42.

TABLE 42 Capture Antibodies Protein Target Vendor Catalog number CloneType Source CD9 R&D Systems MAB1880 209306 IgG2b Mouse (Minneapolis, MN)CD63 BD Biosciences 556019 H5C6 IgG1 Mouse (San Jose, CA) CD81 BDBiosciences 555675 JS-81 IgG1 Mouse PSMA BioLegend (San 342502 LNI-17IgG1, κ Mouse Diego, CA) PCSA Millipore MAB4089 5E10 IgG1 Mouse(Temecula, CA) B7H3 BioLegend 135602 MIH35 IgG2a, κ Rat BioLegend 135604MIH35 IgG2a, κ Rat IL8 GeneTex, Inc. GTX18672 807 IgG1 Mouse (Irvine,CA) GeneTex GTX18649 I8-S2 IgG2b Mouse United States I8430-06A 5D21 IgG1Mouse Biological (Swampscott, MA) Thermo Scientific OMA1-03346 790128G2IgG1κ Mouse (Pierce, Rockford, IL) MCP-1 Novus Biologicals NBP1-42360MNA1 IgG1 Mouse GeneTex GTX18678 S101 IgG1κ Mouse GeneTex GTX18677 S14IgG1κ Mouse Thermo Scientific MA1-81750 2.2-4A4-1A11 IgG1 Mouse (Pierce)TNF-alpha Thermo Scientific HYB 141-09-02 10D9 IgG1 Mouse (Pierce)Thermo Scientific MA1-21386 CH8820 IgG1 Mouse (Pierce) R&D SystemsMAB610 28401 IgG1 Mouse SRVN ProMab Mab-2007128 2H5H2 IgG1 MouseBiotechnologies, Inc. (Richmond, CA) United States S8500-03L 6A189 IgG1Mouse Biological Sigma-Aldrich Co. WH0000332M1 5B10 IgG2aκ Mouse (St.Louis, MO) IL-1B Sigma-Aldrich Co. WH0003553M1 2A8 IgG2bκ Mouse ThermoScientific OMA1-03331 508A4A2 IgG1κ Mouse (Pierce) Thermo ScientificMA1-24785 8516.311 IgG1 Mouse (Pierce) AFP Abcam plc. ab54745 MonoclonalIgGκ Mouse (Cambridge, MA) Abcam ab8201 Polyclonal IgG Rabbit CA-19-9United States C0075-03 1.B.837 IgG1 Mouse Biological BCNP1 Abcam ab59781polyclonal IgG Rabbit BANK1 Abcam ab93203 polyclonal IgG Rabbit CDAAbcam ab35251 polyclonal IgG Sheep LAMN United States L1225-25 2Q601IgG1 Mouse Biological United States L1225-20 2Q592 IgG2a MouseBiological United States L1225-21 2Q596 IgG1κ Mouse Biological M-CSF R&DSystems MAB616 21113 IgG2A Mouse MIF Sigma-Aldrich Co. WH0004282M12A10-4D3 IgG1κ Mouse GeneTex GTX14575 2Ar3 IgG1 Mouse Thermo ScientificMA1-20881 2Ar3 IgG1 Mouse (Pierce) HBD 1 MyBioSource, MBS311954M11-14b-D10 IgG1 Mouse LLC (San Diego, CA) HBD2 MyBioSource MBS311949L12-4C-C2 IgG1 Mouse CRMP-2 AbD Serotec AHP1255 Polyclonal IgG Goat(Raleigh, NC) Abcam ab75036 Polyclonal IgG Rabbit PSME3 Abcam ab91540Polyclonal IgG Rabbit Abcam ab91542 Polyclonal IgG Rabbit MIC1 UnitedStates M1199 9E 99 IgG1 Mouse Biological Reg IV Abcam ab89917MM0254-9B21 IgG2 Mouse Trail-R4 R&D systems MAB633 104918 IgG1 MouseTrail-R2 Thermo Scientific PA1-23497 Polyclonal IgG Rabbit (Pierce) CD44Novus Biologicals, NBP1-04276 5C10 IgG2b Mouse LLC (Littleton, CO)Thermo Scientific MA1-19277 MEM-263 IgG1 Mouse (Pierce) ALIX UnitedStates A1355-64 8J89 IgG1 Mouse Biological Thermo Scientific MA1-839773A9 IgG1 Mouse (Pierce) FASL United States F0019-65B 5E501 IgG1 MouseBiological United States F0019-66V 9i01 IgG1 Mouse Biological L1CAMGeneTex GTX23200 UJ127 IgG1κ Mouse GenWay Biotech, 20-272-193053 UJ181.4IgG Mouse Inc. (San Diego, CA) CRP United States C7907-05A 3H109 IgG1κMouse Biological R&D Systems MAB17071 232007 IgG2b Mouse Abcam ab13426CRP8 Abcam ab76434 CRP135 R&D Systems MAB1707 232026 IgG2a Mouse DLL4Abcam ab61031 Monoclonal IgG2a Mouse Cell Signaling 2589 PolyclonalRabbit Technology, Inc. (Danvers, MA) AURKB Sigma-Aldrich WH0009212M36H7 IgG1κ Mouse Novus Biologicals H00009212-M01A 6A6 IgG1κ Mouse AURKAUnited States A4190-10D 8J339 IgG2b Mouse Biological Thermo ScientificMA1-34566 35C1 IgG2b Mouse (Pierce) SPARC R&D Systems MAB941 122511 IgG1Mouse United States O8063-08 5 E 143 IgG1 Mouse Biological PDGFRBSigma-Aldrich WH0005159M8 4C12 IgG2aκ Mouse R&D Systems MAB1263 PR7212IgG1 Mouse TFF3 Sigma-Aldrich WH0007033M1 Clone 3D9 IgG1κ Mouse DR3United States D4012-01B polyclonal IgG Rabbit Biological MACC-1 ProSci5197 polyclonal IgG Rabbit Incorporated (Poway, CA) MMP7 NovusBiologicals NB300-1000 polyclonal IgG Rabbit TROP2 Santa Cruz sc103908Clone c12 IgG Goat Biotechnology, Inc. (Santa Cruz, CA) Santa Cruzsc80406 Clone yy01 IgG2a Mouse A33 Santa Cruz sc33014 Clone g20 IgG GoatSanta Cruz sc33012 Clone n15 IgG Goat CXCL12 R&D Systems MAB350 79018IgG1 Mouse Sigma Aldrich WH0006387M1 1E 5 IgG2bκ Mouse GDF15 LifeSpanLS-C89472 Clone 9E99 IgG1 Mouse Biosciences, Inc. (Seattle, WA) ASCAAbcam ab19498 polyclonal IgG Rabbit Abcam ab19731 polyclonal IgG RabbitAbcam ab25813 polyclonal IgG Goat VEGFA United States V2110-16A clone5G233 Mouse Biological EphA2 Santa Cruz sc924 Clone c20 Rabbit EGFR BDBiosciences BD555996 Clone EGFR1 Mouse MUC1 Santa Cruz sc7313 CloneVU4H5 Mouse TGM2 Sigma-Aldrich WH0007052M10 Clone 2F4 Mouse TIMP-1Sigma-Aldrich WH0007076M1 Clone 4D12 Mouse GPCR GPR110 GeneTex GTX70591Polyclonal Rabbit MMP9 Novus Biologicals NBP1-28617 Clone SB15C MouseTMEM211 Santa Cruz sc86534 Clone c15 Rabbit UNC93A Santa Cruz sc135539Clone c13 Rabbit CD66e CEA United States C1300-08 polyclonal RabbitBiological CD24 Santa Cruz sc19585 Clone sn3 Mouse CD10 BD Biosciences555373 Clone HI10a Mouse NGAL Santa Cruz sc50350 Clone h130 Rabbit GPR30Abcam ab12563 Polyclonal Rabbit OPN Santa Cruz sc-73631 Clone lfmb-14Mouse MUC17 Santa Cruz sc32602 Clone c19 Goat p53 BioLegend 645702 clonedo.1 Mouse MUC2 Santa Cruz sc15334 Clone H-300 Rabbit Ncam R&D SystemsMAB2408 clone301040 Mouse Tsg 101 Santa Cruz sc-101254 clone Y16J MouseEpcam R&D Systems MAB 9601 MAB 9601 Mouse

Microplex Microspheres with Conjugated Antibody. Capture antibodies areconjugated to desired microspheres selected from fluorescently-dyedcarboxylated MicroPlex® microsphere beads, SeroMAP™ microsphere beadsand MagPlex microsphere beads (Luminex Corporation, Austin, Tex.).Conjugation is performed using protocols supplied by the manufacturer.See “SAMPLE PROTOCOL FOR TWO-STEP CARBODIIMIDE COUPLING OF PROTEIN TOCARBOXYLATED MICROSPHERES,” “SAMPLE PROTOCOL FOR CONFIRMATION OFANTIBODY COUPLING,” and related protocols available online atwww.luminexcorp.com/support/protocols/protein.html. Further details areprovided in the Examples herein.

Exemplary conjugates are shown below in Table 43. Any appropriatecapture antibody can be used for conjugation, e.g., any of those listedabove in Table 42 or other antibodies that target an antigen ofinterest.

TABLE 43 Microsphere Conjugated Antibodies Antibody Catalog Used toConjugate* Bead Region Vendor Number Antigen Antibody Clone 5 LuminexL100-C105-04 CD9 209306 (Austin, TX) 20 Luminex L100-C120-04 CD63 H5C624 Luminex L100-C124-04 CD81 JS-81 15 Luminex L100-C115-04 PSMA LNI-1719 Luminex L100-C119-04 PCSA 5E10 25 Luminex L100-C125-04 B7H3 MIH35*Conjugated antibody information can be found in the Capture Antibodytable above.

Detection Antibodies—various labels can be used. Exemplary antibodies tothe tretrapannins CD9, CD63 and CD81 are shown. Antibodies to otherbiomarkers, e.g., general vesicle biomarkers, cell of origin specificbiomarkers, or disease biomarkers, can be used as desired. See Table 44.

TABLE 44 Detection Antibodies Custom Size Conju- of gation of CustomCatalog Conju- Protein Vendor number gation Clone Type Source Label* CD9BD 624048 4 mg M-L13 IgG1 Mouse PE Bio- sciences CD63 BD 624048 4 mgH5C6 IgG1 Mouse PE Bio- sciences CD81 BD 624048 4 mg JS-81 IgG1 Mouse PEBio- sciences *Phycoerythrin

-   -   a. Phosphate buffered saline (PBS) with BSA, pH 7.4, Sigma,        catalog number P3688-10PAK (Sigma, Saint Louis, Mo., part of        Sigma-Aldrich, Inc.)    -   b. Starting Block Blocking Buffer in PBS, Thermo Scientific,        catalog number 37538 (Thermo Scientific, Part of Thermo Fisher        Scientific, Waltham, Mass.)    -   c. PBS-BN(PBS, 1% BSA, pH 7.4 Sigma Cat# P3688, 0.05% Sodium        Azide, Sigma, catalog number 58032    -   d. Sterile Molecular Grade Water (DNase and RNase Free, 0.1 μM        filtered), Sigma, catalog number W4502    -   e. VCaP microvesicles (2.14 μg/μl)    -   f. Normal Male Plasma, Lot#55-24482-042610 (Innovative Research,        sample 55-24482), used for VCaP control creation

Supplies

-   -   a. USA Scientific TempAssure PCR 8-tube strip, catalog number        1402-2908 (USA Scientific, Inc., Ocala, Fla.)    -   b. Millipore Multiscreen HV Luminex filter plates, 0.45 microM,        clear, styrene, Millipore catalog number MSBVN1250 (Millipore,        Billerica, Mass.)    -   c. USA Scientific co-polymer 1.5 ml non-binding tubes, catalog        number 1415-2500    -   d. USA Scientific TempPlate Sealing foil, catalog number        2923-0110    -   e. Disposable filtered and sterile pipette tips, DNase, RNase        and pyrogen free    -   f. 1L glass bottles, VWR, catalog number 89000-240 (VWR        International, LLC, West Chester, Pa.)    -   g. 250 ml glass bottles, VWR, catalog number 89000-236    -   h. Stir bars, medium, VWR, catalog number 58948-218    -   i. Ice bucket, Fisher, catalog number 02-591-46 (Fisher        Scientific, Part of Thermo Fisher Scientific, Pittsburgh, Pa.)    -   j. 96 well Falcon Plate, VWR, catalog number 62406-321    -   k. 1L graduated cylinder, Fisher, catalog number 03-007-36    -   l. Plate racks, Fisher, catalog number 05-541-55    -   m. Tube racks, Fisher, catalog number 05-541-38    -   n. 4-way racks, Fisher, catalog number 03-448-17    -   o. 15 ml conical, VWR, catalog number 21008-918    -   p. Reagent reservoirs, VWR, catalog number 89094-662    -   q. 10/20 ul filtered pipet tips, Rainin, catalog number GP-L10F        (Rainin Instrument, LLC, Oakland, Calif., a METTLER TOLEDO        Company)    -   r. 200 ul filtered pipet tips, Rainin, catalog number GP-L200F    -   s. 1000 ul filtered pipet tips, Rainin, catalog number GP-L1000F    -   t. 1000 ul non-filtered pipet tips, Rainin, catalog number        GPS-L1000    -   u. Aluminum Foil, Fisher, 01-231-100    -   v. Personal protective equipment    -   w. Master Plan Template Spreadsheet (tracking spreadsheet)

Quality Control:

Assay Controls

Assay controls consist of microvesicles from the VCaP cell line. VCaP isa human epithelial cell line established in 1997 from a vertebral bonemetastasis from a 59 year old Caucasian male patient with hormonerefractory prostate cancer. It was passaged as xenografts in mice thencultured in vitro. The VCaP cell line is androgen sensitive and producesvesicles. See Korenchuk, S., et al., VCaP, a cell-based model system ofhuman prostate cancer. In Vivo, 2001. 15(2): p. 163-68; Jansen, F. H.,et al., Exosomal secretion of cytoplasmic prostate cancerxenograft-derived proteins. Mol Cell Proteomics, 2009. 8(6): p.1192-205.

A triplicate set of VCaP microvesicle (MVS) High and Blank controls arerun on each plate to verify (1) bead master mix performance, (2)individual run technical specifications, and (3) detection antibodyperformance. The VCaP MVS High control consists of 0.5 mg/ml purifiedVCaP microvesicles diluted into normal male plasma. The VCaP MVS Blankcontrol consists of 0 mg/ml purified VCaP microvesicles (i.e. nopurified microvesicles) in a PBS background.

A run is considered valid if the average signal (High VCaP MVS control)to average background (Blank VCaP MVS control) ratio is at least 10-foldabove background. A signal of this magnitude indicates that eachconjugated capture bead has sufficient sample binding capabilities andthat a technically intact run has been performed.

If a run doesn't meet the established metric for the VCaP MVS Controls,then the entire run is repeated. The repeated run will consist of theVCaP MVS controls and an additional plasma concentration of the patientplasma (see Plasma Concentration; Example above). The run is repeated upto two times depending upon number of specimens received. If the runfails the final time, the samples are reported as failed withoutobtaining a valid result.

Internal Controls

The tetraspanin capture antibodies (CD9, CD63, and CD81) serve as aninternal control for adequacy of each sample tested. The average MFI(median fluorescent intensity) is calculated for the three tetraspanincapture antibodies. If the average combined MFI value is greater than500, then the sample is considered to have a sufficient microvesicleconcentration for further testing.

If a sample doesn't meet the established metric for the tetraspanincapture antibodies, then the sample is retested. The repeated runconsists of the VCaP MVS and an additional plasma concentration of thepatient plasma (see Plasma Concentration, Example above). The run willbe repeated up to two more times provided there are additional aliquotsof patient plasma. If the repeated run fails the final time, then thespecimen is reported as Non-evaluable without obtaining a valid result.

Limitations:

If patient plasma is collected or stored improperly, then the vesiclestherein may be degraded or have their protein content altered.Degradation of vesicles may lead to aggregation and false proteinexpression readings, leading to indeterminate or erroneous results.

Procedure:

All steps use filtered tips for pipettes with the exception of the washsteps.

-   -   a. Open appropriate Master Plan Template Spreadsheet and on the        Lot Info tab fill in any empty yellow cells with appropriate        information. Save the file.    -   b. Select the Work Bench Sheet tab (tracking and instruction        worksheet in the Master Plan Template Spreadsheet), and print a        hard copy for use on the bench.    -   c. Remove concentrated plasma samples from the previous day from        refrigerator and place on bench. See Example above for        concentrated plasma preparation.    -   d. Prepare VCaP MVS controls according to recipe on Work Bench        Sheet.        -   i. Fill ice bucket with flaked ice.        -   ii. Remove 1 tube of VCaP MVS Pool (pooled control sample)            and 1 tube of Normal Plasma per plate from −80° C. (−65° C.            to −85° C.) and thaw on ice.        -   iii. Vortex VCaP MVS Pool for 10 seconds at 1600 rpm.        -   iv. Prepare 0.5 ug/ul VCaP Control (refer to Work Bench            Sheet). Normal Plasma tube contains 11.1 μl of normal            plasma; VCaP tube contains 5 μl VCaP. Pipette appropriate            amount of VCaP MVS Pool (orange tube) into Normal Plasma            tube (purple tube) and pipette mix 5 times.        -   v. Place tube in plate rack and incubate for 1 hour at            37° C. (35° C. to 39° C.) in the Jitterbug with shaking at            550 rpm.    -   e. While controls are incubating, prepare Sample Bead Mix.        -   i. Label a new 1.5 ml co-polymer tube with date, initials,            and “Sample Bead Mix.”        -   ii. Add Starting Block and Sample Bead Mix to labeled tube            (refer to Work Bench Sheet).        -   iii. Vortex for 5 seconds on VWR digital vortex at 1600 rpm.        -   iv. Wrap in aluminum foil and incubate on bench top for a            minimum of 10 min.    -   f. While controls are incubating, retrieve the necessary number        of 8-tube strips from storage container based on the plate map        on the Work Bench Sheet. Each strip represents 1 column on the        plate map (maximum number of 8-tube strips per plate is 12).    -   g. Arrange 8-tube strips vertically in plate rack in every other        column and cap each tube. Label tops of tubes starting with 1 at        the top left position and number sequentially top to bottom then        left to right. For example, strips 1-6 are labeled 1-48 in        FIG. 107. Strips 7-12 would be labeled 49-96 on a second plate        rack.    -   h. Transfer 50 μl of each concentrated plasma sample to the        8-tube strips in triplicate according to the plate map on the        Work Bench Sheet.        -   i. Open all tubes except 1, 2, 9, 10, 17, and 18; these are            reserved for the VCaP High and Blank controls.        -   ii. Vortex each concentrated plasma sample tube for at least            5 seconds on digital vortex at 1600 rpm to thoroughly mix            plasma directly before aliquoting into 8-tube strips.        -   iii. Using a p200 pipette, transfer sample to 8-tube strips            closing caps after each addition of a sample.            -   1. Pre-wet each new pipette tip by drawing up 50 μl                concentrated plasma into pipette tip and dispensing back                into sample tube once.            -   2. Using the same pipette tip, draw up another 50 μl                concentrated plasma and dispense into correct tube                making sure to position pipette tip at the bottom of the                tube while dispensing. Bring pipette tip straight out of                tube being careful not to drag pipette tip up the side                of the tube.            -   3. Repeat step 2) above twice until all three tubes for                that sample contain 50 μl concentrated plasma. The same                tip can be used for all three aliquots of the same                sample, but tip should be changed between different                samples.    -   i. After 1 hour VCaP Control incubation, pipette 4 μl of the 0.5        μg/μl VCaP Control into tubes 1, 9, and 17 using a p20 pipette.    -   j. Pipette 4 μl of 1×PBS into tubes 2, 10, and 18.    -   k. Add bead mixture to all samples.        -   i. Open all tubes.        -   ii. Vortex Sample Bead Mix for 5 seconds on digital vortex            at 1600 rpm. Repeat this vortex step prior to each            successive pipette aspiration.        -   iii. Using a 200 μl electronic repeater pipette, add 4 μl of            bead mixture to each sample tube, including control tubes,            closing caps after each addition of beads.        -   iv. Perform a quick 1 second spin in a mini-galaxy            centrifuge of the 8-tube strips to collect all liquid at the            bottom of the tube.        -   v. Incubate in 37° C. (35° C. to 39° C.) Jitterbug at 550            rpm for 2 hours.        -   vi. Any excess beads may be wrapped in aluminum foil and            kept at 4° C. (2° C. to 8° C.) overnight for use as overage            the following day. This usage of leftover beads may continue            for one week, but each Monday any old beads should be            discarded in biohazard waste bin.    -   l. During 2 hour incubation, prepare Detector Antibodies.        -   i. Label 15 ml conical tube with date, initials and            “Detector Antibodies.”        -   ii. Add PBS-BN to 15 ml conical (refer to Work Bench Sheet            for volume).        -   iii. Add CD9, CD81, and CD63 to PBS-BN (refer to Work Bench            Sheet for volumes).        -   iv. Vortex for 5 seconds on VWR digital vortex at 1600 rpm.        -   v. Wrap in aluminum foil and place in 4-way rack until use.    -   m. Fill a disposable reservoir with PBS-BN.    -   n. If there are less than 23 samples on plate, cut a foil seal        with scissors to cover any empty columns and stick over empty        columns on plate.    -   o. During the following steps, pipette tips should never touch        the bottom of the filter plate wells.

Always touch tips to the side of the wells. Also, the vacuum shouldalways operate between 3 inches and 5 inches Hg. Plates should only beon vacuum manifold during aspirations; during all other steps plateshould be placed on bench top.

-   -   p. Using a 1000 ul electronic multichannel pipette and 1000 ul        non-filtered tips, pre-wet a 1.2 μm Millipore filter plate with        100 μl/well of PBS-BN and aspirate by vacuum manifold.    -   q. Press vacuum release button before removing plate from        manifold. Blot bottom of plate dry on a clean paper towel.    -   r. Using a p200 multichannel pipette, add 150 μl of PBS-BN to        each well of the plate.    -   s. After 2 hour incubation, remove samples from Jitterbug and        perform a quick 1 second spin in a mini-galaxy centrifuge.    -   t. Transfer the incubated samples to the Millipore filter plate        following the plate map on the Work Bench Sheet.        -   i. Working left to right across the plate (columns 1-12)            remove one 8-tube strip at a time from plate rack and place            in an empty plate rack. Verify that 8-tube strips are used            in chronological order by double-checking that the numbers            written on the caps are in proper order and orientation.        -   ii. Using a p20 multichannel pipette, transfer the two            controls in the 8-tube strip to the proper wells on the            filter plate. Pipette mix 5 times during aspiration to            ensure that all beads are in solution before dispensing in            filter plate, pipette mixing twice in the PBS-BN.        -   iii. Using a p200 multichannel pipette, transfer all plasma            samples to the filter plate.        -   iv. Concentrated plasma can be extremely viscous, so slowly            pipette mix each sample 5 times ensuring that plasma is            travelling up and down inside the pipette tip. If a sample            is not travelling, increase number of pipette mixes until            each sample has been mixed at least 5 times. Transfer            samples to filter plate pipette mixing twice in the PBS-BN.        -   v. After each 8-tube strip is empty, verify that all            contents are gone before discarding strip in biohazard waste            bin.        -   vi. If any liquid remains in any of the tubes, repeat steps            i)-iv) above.        -   vii. Continue steps i)-v) above until all samples have been            added to the filter plate.    -   u. If at any point during the steps below any well clogs but        other wells are aspirated, continue constant vacuum for 5        seconds. If some sample is still clogged and not moving through        the filter, mark that well(s) on the plate (with a marker) and        on the Work Bench Sheet. Aspirate liquid out of well(s) using a        p1000 pipette and leave that well(s) empty during all successive        steps.    -   v. Aspirate the supernatant by vacuum manifold slowly. Press        vacuum release button before removing plate from manifold. Blot        bottom of plate dry on a clean paper towel.    -   w. Using a p1000 electronic multichannel pipette and 1000 ul        NON-FILTERED tips, wash each well with 200 μl of PBS-BN,        aspirate, press vacuum release button, then remove plate from        manifold and thoroughly blot bottom of plate on a clean paper        towel.    -   x. Repeat previous step for a total of 2 washes with 200 ul        PBS-BN per wash.    -   y. Using a p200 multichannel pipette, add 50 μl of PBS-BN to        each well.    -   z. Using a p1000 electronic single channel pipette, add 50 μl of        the diluted detection antibody (from above) to each well.        -   i. Any excess detection antibody may be wrapped in aluminum            foil and kept at 4° C. (2° C. to 8° C.) overnight for use as            overage the following day. This usage of leftover detection            antibodies may continue for one week, but any old detection            antibodies should be discarded in a biohazard waste bin.    -   aa. Cover the filter plate using a foil plate seal. Gently seal        the foil along the outside perimeter, being careful not to        create positive pressure within the wells as this will force        liquid out the bottom of the filter.    -   bb. Incubate at 25° C. (22° C. to 27° C.) on the Jitterbug at        550 rpm for 1 hour.    -   cc. During 1 hour incubation refer to Luminex Maintenance and        Calibration SOP (MA-25-0009) and perform all necessary        maintenance and/or calibrations on the Luminex bead reader        machine.    -   dd. After maintenance and/or calibrations are complete, enter        plate information in Luminex software.        -   i. Open xPONENT 3.1 software and log in.        -   ii. Click the Batches tab.            -   Under Batch Name, enter the ID of the plate which is                found near the top of the Work Bench Sheet but add an                additional underscore and 1 at the end.                -   1. ID: 20100915_SampleV1_PME_(—)1        -   iii. This later denotes the upload number into the data            store. For example:            -   1. Batch Name: 20100915_SampleV1_PME_(—)1_(—)1        -   iv. Click on Create a New Batch from an Existing Protocol            and select the desired protocol.        -   v. Click Next.        -   vi. Highlight wells containing samples and controls by            clicking and dragging on the plate map.        -   vii. Click the Unknown button below the plate map.        -   viii. On the right side of the screen click the Import List            button and navigate to the appropriate exported text file,            select it, then click Open.    -   ee. After 1 hour incubation of samples, remove filter plate from        Jitterbug, remove foil seal and aspirate the supernatant by        vacuum manifold. Press vacuum release button then remove plate        from manifold and thoroughly blot bottom of plate on a clean        paper towel.

ff. Using a p1000 electronic multichannel pipette and 1000 ulnon-filtered tips, wash each well with 100 μl of PBS-BN, aspirate, pressvacuum release button, then remove plate from manifold and thoroughlyblot bottom of plate on a clean paper towel.

gg. Repeat previous step for a total of 2 washes with 100 ul PBS-BN perwash.

-   -   hh. Using a p200 multichannel pipette, add 100 μl of PBS-BN to        each well.    -   ii. Cover the filter plate using a foil plate seal. Gently seal        the foil along the outside perimeter.    -   jj. Place plate on VWR microplate shaker for 20 seconds at 950        rpm. Analyze plate on Luminex 200 machine.        -   i. Retrieve plate from shaker and remove foil seal.        -   ii. Click the Eject button at the bottom of the screen.        -   iii. Place plate on drawer (A1 goes in top left corner).        -   iv. Click the Retract button at the bottom of the screen.        -   v. Click the Run Batch button in the lower right corner of            the screen.        -   vi. Click OK in the pop-up window.    -   kk. At the conclusion of the run, go to the Results tab, select        Saved Batches on the left, highlight the run and click on Exp        Results at the bottom of the screen to export a .csv file.    -   ll. Save the .csv file to the appropriate network server        location.    -   mm. Log into the data analysis software, go to the Lab Queues        tab and select Import Results near the top right corner.    -   nn. Click Browse and navigate to the .csv on the clinical drive        and click Open.    -   oo. Verify that the data in the data analysis software is        consistent with that exported from the Luminex 200 machine.

Example 51 Analysis of Prostate Cancer (PCa) Vesicles Using MultiplexAssays

In this example, plasma samples from patients with prostate cancer (PCa)or without PCa (normals) are analyzed according to the general procedureoutlined in Example 27. Plasma is prepared according to the protocol ofExample 49 and multiplex analysis is performed as in Example 50. Captureantibodies to the vesicle surface antigen proteins in Table 45 were usedto screen for biomarkers that detect PCa.

TABLE 45 PCa Capture Antibodies Protein Catalog Target Antibody VendorNumber(s) SPB Anti surfactant protein-B antibody US Biological S8401-02IL-8 Anti interleukin 8 antibody US biological I8430-06A SPC Antisurfactant protein-C antibody US Biological U2575-03 IL8 AntiInterleukin 8 antibody Thermo scientific OMA1-03346 pierce MUC1 MUC 1aptamer 2_3′ amino.mod IDT 55403591 seq1 TFF3 Anti Trefoil factor 3(intestinal) antibody Sigma-Aldrich WH0007033M1 TF (FL- Anti tissuefactor (coagulation factor III) antibody Santacruz sc-20160 295) MUC1MUC 1 aptamer 4_3′ amino.mod IDT 55403593 seq3 PGP9.5 Anti protein Gproduct 9 antibody Genway 20-002-35062 MCP-1 Anti monocytechemoattractant antibody Novus Biologicals NBP1-42360 CD9 Anti Clusterof Differentiation 9 antibody Novus biologicals NBP1-28363 CD9 AntiCluster of differentiation 9 antibody R&D systems MAB1880 MCP-1 Antimonocyte chemoattractant antibody Genetex GTX18678 MS4A15c11 Antimembrane spanning 4A1 antibody Sigma WH0000931M1 EphA2 Anti Ephrin-Areceptor antibody Santa Cruz sc924 HSP70 Anti heat shock proteinantibody Biolegend 648002 TNF- Anti tumor necrosis factor-alpha antibodyThermo scientific HYB 141-09-02 alpha pierce TIMP2 Anti tissue inhibitorof metallo proteinase 2 antibody Genetex GTX48556 GAL3 Anti Galactosemetabolism regulator 3 antibody Santa Cruz sc-32790 TNF- Anti tumornecrosis factor-alpha antibody Thermo scientific MA1-21386 alpha pierceSRVN Anti survivin antibody US biologicals S8500-03L INSIG-2 Antiinsulin induced gene 2 antibody Santa Cruz sc-66936 PTEN Antiphosphatase and tensin homolog antibody Sigma Aldrich WH0005728M1- 100UGSRVN Anti survivin antibody Sigma Aldrich WH0000332M1 MIS RII AntiMnllerian inhibiting substance receptor II R&D AF4749 antibody HER2 AntiHuman Epidermal growth factor Receptor 2 R&D MAB1129 (ErbB2) antibodyEGFR Anti epidermal growth factor antibody BD biosciences 555996 N-galAnti Neutrophil gelatinase-associated lipocalin Santa Cruz sc-18698antibody IL-1B Anti Interleukin-1B antibody Sigma Aldrich WH0003553M1 ERAnti estrogen receptor antibody US Biological E3564-89 SPP1 Anti-SPP1Sigma WH0006696M1 iC3b Anti human inactive complement component 3bThermo MA1-82814 antibody AFP Anti alpha fetal protein antibody Abcamab54745 PSMA Anti prostate-specific membrane antibody Biolegend 342502BPSMA Anti Prostate-specific membrane antibody Genetex GTX19071 PSMA AntiProstate-specific membrane antibody 342502 AFP Anti alpha fetal proteinantibody Abcam ab8201 KLK2 Anti kallikrein-related peptidase 2 antibodyNovus Biologicals H00003817-M03 PR (B) Anti progesterone R antibody R&DPP-H5344-00 MRP8 Anti Migration inhibitory factor-related protein 8 USBiological M4688-36A antibody CA-19-9 Anti carbohydrate 19-9 antibody USBiological C0075-13B BCNP1 Anti B-cell novel protein1 antibody abcamab59781 BANK1 Anti B-cell scaffold protein with ankyrin repeats 1 abcamab93203 antibody PCSA Anti-Prostate Cell Surface antibody MilliporeMAB4089 PCSA Anti-Prostate Cell Surface antibody In house CD63 AntiCluster of Differentiation 63 antibody R&D systems MAB5048 CD63 AntiCluster of differentiation 63 antibody BD biosciences 556019 B7H4 Antiimmune cosmitulatory protein antibody US Biological B0000-35A CDA Anticytidine deaminase antibody abcam ab35251 MUC1 Anti Mucin 1, cellsurface associated protein antibody Santa Cruz sc7313 TGM2 AntiTransglutaminase-2 antibody Sigma-Aldrich WH0007052M10 DLL4 Anti Deltalike protein 4 antibody Santa Cruz sc-18639 CD81 Anti Cluster ofDifferentiation 81 antibody Sigma Aldrich WH0000975M1 CD81 Anti Clusterof differentiation 81 antibody BD biosciences 555675 B7H3 Anti Clusterof Differentiation 276(B7 homolog 3) R&D systems MAB1027 antibody B7H3Anti Cluster of differentiation 276 antibody BioLegend 135602 LAMN AntiLaminin antibody US Biological L1225-25 HER 3 Anti Human Epidermalgrowth factor Receptor 3 US Biological E3451-36A (ErbB3) antibody CRPAnti c-reactive protein antibody abcam ab76434 MART-1 Anti MelanomaAntigen Recognized by T-cells 1 US Biological M2410 antibody LAMN AntiLaminin antibody US Biological L1225-20 M-CSF Anti Macrophagecolony-stimulating factor antibody R&D systems MAB616 PSA Anti Prostatespecific antibody My bioscource MBS312739 MFG-E8 Anti milk fatglobule-EGF factor 8 protein antibody R&D systems MAB27671 CD46 AntiCluster of differentiation 46 antibody R&D systems MAB2005 STAT 3 AntiSignal transducer and activator of transcription 3 US BiologicalS7971-01M antibody MIF Anti macrophage migration inhibihitory factorGenetex GTX14575 antibody TIMP-1 Anti tissue inhibitor of metalloproteinase-1 antibody Sino biological 10934-MM02 Inc MACC-1 AntiMetastatis associated in colon cancer-1 antibody ProSciInc 5197 PSA Antiprostate specific antigen antibody R&D MAB13442 ALP Anti ApolipoproteinJ antibody Novus Biologicals H00001191-M02 CRP Anti c-reactive proteinantibody Abcam ab13426 HBD2 Anti human beta defensin 2 antibody Mybiosource MBS311949 TrKB Anti tyrosine Kinase B antibody Novusbiologicals NB100-92063 (poly) MMP26 Anti matrix metallo proteinase 26antibody Novus Biologicals H00056547-M02 AnnV Anti-AnnexinV antibodyAbcam ab54775 CD24 Anti Cluster of differntiation 24 antibody (Heat BDbiosciences bd 555426 Stable antigen) VEGF A Anti Vascular endothelialgrowth factor A antibody US Biological V2110-05D PCSA Anti prostate cellsurface antibody Novus Biologicals H00008000-M03 TIMP-1 Anti Tissueinhibitor of metallo proteinase-1 antibody Sigma-Aldrich WH0007076M1 IL6Unc Anti interleukin 6 unconjugated antibody Invitrogen AHC0762 CRMP-2Anti collapsin response mediator protein 2 antibody Abcam ab75036 IL 6Unc Anti human interleukin 6 unconjugated antibody Invitrogen AHC0762OPG Anti osteoprotegerin antibody Biovendor RD182003110- 13 PSME3 Antiproteasome activator complex subunit 3 Abcam ab91540 antibody PBP AntiProstatic binding protein antibody Novus Biologicals H00005037-M01 PSME3Anti proteasome activator complex subunit 3 Abcam ab91542 antibody IL6RAnti interleukin 6 receptor antibody Sigma aldrich WH0003570M1 CD59(MEM-Anti Cluster of differentiation 59 (MEM-43) antibody Genetex GTX7462043) PIM1 Anti proviral integration site antibody Novus BiologicalsH00005292-M08 GPCR Anti G-protein coupled receptor antibody (vendorGeneTex GTX70593 replacement) EphA2 Anti Ephrin-A receptor 2 antibodySanta Cruz sc-10746 (H-77) MIC1 Anti macrophage inhibitory cytokineantibody US Biologicals M1199 Reg IV Anti Regenerating islet-derivedfamily, member 4 Abcam ab89917 antibody MMP9 Anti MatrixmetalloProteinase 9 antibody Novus biologicals NBP1-28617 PRL Anti prolactinMonoclonal antibody Thermo Scientific MA1-10597 Pierce Trail-R4 AntiTNF-related apoptosis-inducing ligand receptor R&D systems MAB633 4antibody EphA2 Anti Ephrin-A receptor 2 antibody Santa Cruz sc101377Trail-R2 Anti TNF-related apoptosis-inducing ligand receptor Thermoscientific PA1-23497 2 antibody pierce STEAP1 Anti Six TransmembraneEpithelial Antigene of the US Biological S7500-02 Prostate 1 antibodyPA2G4 Anti Proliferation-associated protein 2G4 antibody Sigma aldrichSAB2101707 MMP7 Anti Matrix metallo Proteinase 7 antibody Novusbiologicals NB300-1000 TMEM211 Anti Tumor Microenvironment of Metastasis211 Santa Cruz sc86534 antibody EZH2 Anti Histone-lysineN-methyltransferase antibody Sigma aldrich WH0002146M1 TROP2 Anti humantrophoblast cell-surface antibody Santa Cruz sc103908 TMPRSS2 AntiTransmembrane protease, serine 2 antibody Abcam ab92323 EZH2 AntiHistone-lysine N-methyltransferase antibody ABD serotec MCA4898Z PCSAAnti Prostate specific antibody Novus Biologicals NB100-66506 CD44 AntiCluster of differntiation 44 antibody Novus Biologicals NBP1-04276 SCRN1Anti secrin-1 antibody Sigma-Aldrich HPA024517 CD44 Anti Cluster ofdifferntiation 44 antibody Thermo scientific MA1-19277 pierce RUNX2 Antirunt-related transcription factor 2 antibody Sigma aldrich WH0000860M1CD1.1 Anti cyclin D1 antibody GTX26152 22995 EphA2 Anti Ephrin-Areceptor 2 antibody Santa Cruz sc135658 TWEAK Anti Tumor necrosis factorlike weak inducer of US biological T9185-01 apoptosis ALIX AntiApoptotic linked gene product 2 Interacting US Biologicals A1355-64Protein X antibody SERPINB3 Anti serpin peptidase inhibitor, clade Bmember 3 Sigma aldrich WH0006317M1 antibody ALIX Anti Apoptotic linkedgene product 2 Interacting Thermo scientific MA1-83977 Protein Xantibody pierce Trop2 Anti human trophoblast cell-surface antibody SantaCruz sc80406 CDAC11a2 Anti Cytidine Deaminase antibody Sigma WH0081602M1FASL Anti human Fas Ligand antibody US Biologicals F0019-65B BCA-225Anti breast cancer antigen 225 antibody US Biological B0395-10B EGFRAnti epidermal growth factor antibody R&D AF231 UNC93A Anti unc 3homolog A antibody Santa Cruz sc135539 FASL Anti human Fas Ligandantibody US Biologicals F0019-66V ALA Anti serum amyloid A antibodyabcam ab18713 DR3 Anti death receptor 3 (apoptosis inducing) antibody USBiological D4012-01B BRCA Anti breast cancer gene antibody US BiologicalB2708-06 L1CAM Anti L1 cell adhesion molecule antibody Genway20-272-193053 UNC93A Anti unc 93 homolog A antibody Santa Cruz sc135541APC Anti adenomatous polyposis antibody US biological A2298-70A CRP Antic-reactive protein antibody US Biologicals C7907-05A CRP Anti c-reactiveprotein antibody R&D systems MAB17071 CA125 Anti carbohydrate antigen125 antibody US Biological C0050-01D (MUC16) A33 Anti glyco protein a33antibody Santa Cruz sc33014 MMG Anti mammaglobin antibody Santa Cruzsc-48328 CD174 Anti Cluster of differentiation174 antibody US BiologicalL2056 (Lewis y) DLL4 Anti Delta like protein 4 antibody Abcam ab61031a33 n15 Anti glyco protein a33 antibody Santa Cruz sc-33012 MUC1 MUC 1aptamer 11_5′ amino.mod IDT 55403589 seq11A CD66e Anti Carcinoembryonicantigen (CEA, CD66e) US biological C1300-08 CEA DLL4 Anti Delta likeprotein 4 antibody Cell signaling 2589 technology NPGP/NPFF2 AntiNeuropeptide FF receptor 2 antibody Santa Cruz sc-46206 TIMP1 AntiTissue inhibitor of metallo proteinase-1 antibody US BiologicalT5580-08B AURKB Anti Aurora Bkinase (serine/threonine-protein kinaseSigma Aldrich WH0009212M3 6) antibody CD24 Anti Cluster ofdifferntiation 24 antibody (Heat Santa Cruz sc19585 Stable antigen)TIMP1 Anti tissue inhibitor of metallo proteinase 1 antibody R&D systemsMAB970 AURKB Anti Aurora Bkinase (serine/threonine-protein kinase NovusBiologicals H00009212- 6) antibody M01A SPDEF Anti SAM pointed domaincontaining ets Novus Biologicals H00025803-M01 transcription factorantibody C-erbB2 Anti c-erb2 antibody BD Biosciences 610161 CD10 AntiCluster of differntiation 10 antibody (Heat BD Pharmingen 555373 Stableantigen) AURKA Anti Aurora A kinase (serine/threonine-protein kinase USBiologicals A4190-10D 6) antibody BDNF us Anti Brain derivedneutrotrophic factor antibody US Bio B2700-02D bio AURKA Anti Aurora Akinase (serine/threonine-protein kinase Thermo scientific MA1-34566 6)antibody pierce FRT c.f23 Anti Ferritin antibody Santa Cruz sc-51887 FRTAnti Ferritin (heavy chain) antibody Santa Cruz sc-51888 N-GAL AntiNeutrophil gelatinase-associated lipocalin Santa Cruz sc18695 antibodySPARC Anti Secreted protein acidic antibody rich in cysteine R&D systemsMAB941 antibody CD73 Anti Secreted protein acidic antibody rich incysteine R&D systems MAB5795 antibody seprase Anti seprase antibody R&DMAB3715 NGAL Anti Neutrophil gelatinase-associated lipocalin Santa Cruzsc50350 antibody Epcam Anti Epithelial cellular adhesion moleculeantibody R&D systems MAB 9601 PDGFRB Anti platelet-derived growth factorSigma Aldrich WH0005159M8 receptor, beta, subunit antibody CEA Anticarcinoembryogenic antibody US Biological C1300-01B NGAL Anti Neutrophilgelatinase-associated lipocalin Santa Cruz sc59622 antibody GPR30 AntiG-protein receptor antibody GeneTex GTX100001 PDGFRB Antiplatelet-derived growth factor R&D systems MAB1263 receptor, beta,subunit antibody MUC17 Anti Mucin 17, cell surface associated proteinSanta Cruz sc32600 antibody CYFRA21-1 Anti cytokeratin 19 fragmentantibody MedixMab 102221 C-Erb2 Anti c-erb2 antibody US biologicalC00026-11NX ASCA Anti saccharomyces cerevisiae antibody abcam ab19731ASCA Anti saccharomyces cerevisiae antibody abcam ab19498 p53 Anti tumorprotein 53 antibody BioLegend 645802 OPN Anti osteopontin antibody SantaCruz sc-21742 MAPK4 Anti mitogen activated protein kinase 4 antibodySigma-Aldrich WH0005596M2 C-Bir Anti Flagellin antibody abcam ab93713LDH Anti lactate dehydrogenase antibody US Bio L1011-12H OPN Antiosteopontin antibody Santa Cruz sc-73631 ASPH Anti Aspartyl/asparaginylβ-hydroxylase(DO1 P) Novus H00000444- (D01P) antibody D01P HSP Anti heatshock protein antibody US Bio H1830-94G OPN Anti osteopontin antibody(replacement for OST zz09 R&D systems MAB14332 santacruz sc-80262) OPNAnti osteopontin antibody Santa Cruz sc-80262 ASPH AntiAspartyl/asparaginyl β-hydroxylase(DO3) Novus H00000444-D03 (D03)antibody ASCA Anti saccharomyces cerevisiae antibody abcam ab25813CXCL12 Anti Chemokine (C—X—C motif) ligand 12 antibody Sigma AldrichWH0006387M1 ASPH Anti Aspartyl/asparaginyl β-hydroxylase(G-20) SantaCruz sc-33367 (G-20) antibody p53 Anti tumor protein 53 antibodyBioLegend 628202 MUC17 Anti Mucin 17, cell surface associated proteinSanta Cruz sc32602 antibody OPN Anti osteopontin antibody R&D MAB1433HBD 1 Anti human beta defensin 1 antibody My biosource MBS311954 CRMP-2Anti collapsin response mediator protein 2 antibody AbD Serotec AHP1255Trop2 Anti human trophoblast cell-surface antibody Santa Cruz sc103909OPN Anti osteopontin antibody R&D MAB14332 hVEGFR2 Anti VascularEndothelial Growth Factor Receptor 2 R&D systems MAB3572 antibody p53Anti tumor protein 53 antibody BioLegend 645702 OPN Anti osteopontinantibody R&D MAB14331 ASPH Anti Aspartyl/asparaginylβ-hydroxylase(H-300) Santa Cruz sc-66939 (H-300) antibody MUC2 AntiMucin 2, cell surface associated protein antibody Santa Cruz sc15334Muc2 Anti mucin 2 antibody Santa Cruz sc-15334 Ncam Anti-hNCAM/CD56antibody R&D MAB2408 CXCL12 Anti Chemokine (C—X—C motif) ligand 12antibody R&D systems MAB350 CD10 Anti Cluster of differntiation 10antibody Santa Cruz sc19993 ASPH Anti Aspartyl/asparaginylβ-hydroxylase(A10) Santa Cruz sc-271391 (A-10) antibody HAP Antihaptoglobin antibody USBIO H1820-05 CRP Anti c-reactive protein antibodyUS Bio C7907-05A ASPH Anti Aspartyl/asparaginyl β-hydroxylase(666-680)Sigma-Aldrich A7110 (666-680) antibody Gro-alpha Anti Human gro alphaantibody GeneTex GTX10376 ErbB4 Anti Human Epidermal growth factorReceptor 4 US Biological E3451-40F antibody Tsg 101 Anti tumorsusceptibility gene 101 antibody Santacruz sc-101254 GDF15 Anti Growthdifferentiation factor 15 antibody Life span LS-C89472 biosciences ASPHAnti Aspartyl/asparaginyl β-hydroxylase(246-260) Sigma-Aldrich A6985(246-260) antibody

As indicated in Table 45, multiple antibodies to a single targetbiomarker are tested as available and as desired. This allows forassessing vesicle capture using different surface epitopes to determinewhich provide the desired performance for detecting PCa.

Example 52 Analysis of Colorectal Cancer (CRC) Vesicles Using MultiplexAssays

In this example, plasma samples from patients with colorectal cancer orwithout CRC (normals) are analyzed according to the general procedureoutlined in Example 27. Plasma is prepared according to the protocol ofExample 49 and multiplex analysis is performed as in Example 50. Captureantibodies to the vesicle surface antigen proteins in Table 46 were usedto screen for biomarkers that detect CRC.

TABLE 46 CRC Capture Antibodies Protein Target Antibody Vendor CatalogNumber(s) A33 Anti human glycoprotein A33 Santa Cruz sc50522, sc33014,sc33012 AFP Anti alpha fetal protein antibody Abcam ab8201, ab54745 ALIXAnti Apoptotic linked gene product 2 Thermo Scientific MA1-83977Interacting Protein X antibody (Pierce) United States A1355-64Biological ALX4 Anti aristaless-like homeobox 4 antibody ANCAAntineutrophil cytoplasmic antibody APC Anti adenomatous polyposisantibody ASCA Anti saccharomyces cerevisiae antibody Abcam ab19498,ab19731, ab25813 AURKA Anti Aurora A kinase (serine/threonine- ThermoScientific MA1-34566 protein kinase 6) antibody (Pierce) United StatesA4190-10D Biological AURKB Anti Aurora B kinase (serine/threonine- NovusBiologicals H00009212-M01A protein kinase 6) antibody Sigma-AldrichWH0009212M3 B7H3 Anti Cluster of differntiation 276 BioLegend 135602,135604 antibody BANK1 Anti B-cell scaffold protein with ankyrin Abcamab93203 repeats 1 antibody BCNP1 Anti B-cell novel protein1 antibodyAbcam ab59781 BDNF Anti Brain derived neutrotrophic factor United StatesB2700-02D antibody Biological CA-19-9 Anti carbohydrate 19-9 antibodyUnited States C0075-03 Biological CCSA-2 Anti colon cancer-specific2antibody CCSA-3&4 Anti colon cancer-specific3,4 antibody CD10 AntiCluster of differntiation 10 antibody BD Biosciences 555373 Santa Cruzsc19993 CD24 Anti Cluster of differntiation 24 antibody BD Biosciencesbd 555426 Santa Cruz sc19585 CD44 Anti Cluster of differentiation 44Novus Biologicals, NBP1-04276 antibody LLC Thermo Scientific MA1-19277(Pierce) CD63 Anti Cluster of differntiation 63 antibody BD Biosciences556019 CD66 CEA Anti carcinoembryonic antigen (CEA) Santa Cruz family(CD66) antibody CD66e CEA Anti Carcinoembryonic antigen United StatesC1300-08 (CEA, CD66e) Biological CD81 Anti Cluster of differntiation 81antibody BD Biosciences 555675 CD9 Anti Cluster of differntiation 9antibody R&D Systems MAB1880 CDA Anti cytidine deaminase antibody Abcamab35251 Sigma-Aldrich WH0081602M1 C-Erb2 Anti c-erb2 antibody BDBiosciences 610161 United States C00026-11N1 Biological CRMP-2 Anticollapsin response mediator protein Abcam ab75036 2 antibody AbD SerotecAHP1255 (Raleigh, NC) CRP Anti c-reactive protein antibody Abcamab13426, ab76434 R&D Systems MAB1707 United States C7907-05A BiologicalCRTN Anti cortractin antibody CXCL12 Anti Chemokine (C—X—C motif) ligandR&D Systems MAB350 12 antibody Sigma Aldrich WH0006387M1 CYFRA21-1CYFRA21-1 Medix Biochemica 102221 (Kauniainen, Finland) DcR3 Anti decoyreceptor3 antibody DLL4 Anti Delta like protein 4 antibody Abcam ab61031Cell Signaling 2589 Technology, Inc. DR3 Anti death receptor 3(apoptosis United States D4012-01B inducing) antibody Biological UnitedStates D4012-01B Biological EGFR Anti epidermal growth factor antibodyBD Biosciences BD555996 R&D Systems AF231 Epcam Anti Epithelial cellularadhesion R&D Systems MAB 9601 molecule antibody EphA2 Anti Ephrin-Areceptor antibody Santa Cruz sc924, sc10746, sc101377, sc135658 FASLAnti human Fas Ligand antibody United States F0019-65B, F0019-Biological 66V FRT Anti Ferritin antibody Santa Cruz sc51887, sc51888GAL3 Anti Galactose metabolism regulator 3 Santa Cruz sc32790 antibodyGDF15 Anti Growth differentiation factor 15 LifeSpan LS-C89472 antibodyBiosciences GPCR Anti G-protein coupled receptor antibody GeneTexGTX70591 GPR110 GPR30 Anti GPR30 antibody Abcam ab12563 GRO-1 Anti Humangro alpha antibody Genetex Inc GTX10376 HBD 1 Anti human beta defensin 1antibody MyBioSource, LLC MBS311954 HBD2 Anti human beta defensin 2antibody MyBioSource MBS311949 HNP1-3 Anti human neutrophil peptide 1-3antibody IL-1B Anti Interleukin-1B antibody Sigma-Aldrich Co.WH0003553M1 Thermo Scientific OMA1-03331, (Pierce) MA1-24785 IL8 AntiInterleukin 8 antibody GeneTex, Inc. GTX18649, GTX18672 ThermoScientific OMA1-03346 (Pierce) United States I8430-06A Biological IMP3Anti Insulin-like growth factor-II mRNA-binding protein 3 antibody L1CAMAnti L1 cell adhesion molecule antibody GeneTex GTX23200 GenWay Biotech,20-272-193053 Inc. LAMN Anti Laminin antibody United States L1225-20,L1225- Biological 25, L1225-21 MACC-1 Anti Metastatis associated incolon ProSci Incorporated 5197 cancer-1 antibody MGC20553 Anti FERMdomain containing 3 antibody MCP-1 Anti monocyte chemoattractantantibody GeneTex GTX18677, GTX18678 Novus Biologicals NBP1-42360 ThermoScientific MA1-81750 (Pierce) M-CSF Anti Macrophage colony-stimulatingR&D Systems MAB616 factor antibody MIC1 Anti macrophage inhibitorycytokine United States M1199 antibody Biological MIF Anti macrophagemigration inhibihitory GeneTex GTX14575 factor antibody Sigma-AldrichCo. WH0004282M1 Thermo Scientific MA1-20881 (Pierce) MMP7 Anti Matrixmetallo Proteinase 7 Novus Biologicals NB300-1000 antibody MMP9 AntiMatrixmetallo Proteinase 9 Novus Biologicals NBP1-28617 antibody MS4A1Anti membrane spanning 4A1 antibody Sigma-Aldrich WH0000931M1 MUC1 AntiMucin 1, cell surface associated Santa Cruz sc7313 protein antibodyMUC17 Anti Mucin 17, cell surface associated Santa Cruz sc32600, sc32602protein antibody MUC2 Anti Mucin 2, cell surface associated Santa Cruzsc15334 protein antibody Ncam Anti Ncam R&D Systems MAB2408 NGAL AntiNeutrophil gelatinase-associated Santa Cruz sc18695, sc32600, lipocalinantibody sc50350, sc59622 NNMT Anti nicotinamide N-methyltransferaseantibody OPN Anti osteopontin antibody Santa Cruz sc-21742, sc-73631, sc80262 p53 Anti p53 BioLegend 628202, 645702, 645802 PCSA Anti prostatecell surface antigen Millipore MAB4089, RA1004150 PDGFRB Antiplatelet-derived growth factor R&D Systems MAB1263 receptor, beta,subunit antibody Sigma-Aldrich WH0005159M8 PRL Anti prolactin Monoclonalantibody Thermo Scientific MA1-10597 Pierce PSMA Anti prostate specificmembrane antigen BioLegend 342502 PSME3 Anti proteasome activatorcomplex Abcam ab91540, ab91542 subunit 3 antibody Reg IV AntiRegenerating islet-derived family, Abcam ab89917 member 4 antibody SCRN1Anti secrin-1 antibody Sigma-Aldrich HPA024517 Sept-9 Anti septin9antibody SPARC Anti Aurora A kinase (serine/threonine- R&D SystemsMAB941 protein kinase 6) antibody United States O8063-08 BiologicalSPON2 Anti (Spondin2)Extracelllular matrix protein antibody SPR AntiSeprase (FAB) antibody SRVN Anti tumor necrosis factor-alpha ProMabMab-2007128 antibody Biotechnologies, Inc. Sigma-Aldrich Co. WH0000332M1United States S8500-03L Biological TFF3 Anti Trefoil factor 3(intestinal) antibody Sigma-Aldrich WH0007033M1 TGM2 AntiTransglutaminase-2 antibody Sigma-Aldrich WH0007052M10 TIMP-1 AntiTissue inhibitor of metallo Sigma-Aldrich WH0007076M1 proteinase-1antibody TMEM211 Anti Tumor Microenvironment of Santa Cruz sc86534Metastasis 211 antibody TNF-alpha Anti tumor necrosis factor-alpha R&DSystems MAB610 antibody Thermo Scientific HYB 141-09-02, (Pierce)MA1-21386 TPA Anti tissue polpeptide antibody TPS Anti Tissuepolypeptide-specific antibody Trail-R2 Anti TNF-relatedapoptosis-inducing Thermo Scientific PA1-23497 ligand receptor 2antibody (Pierce) Trail-R4 Anti TNF-related apoptosis-inducing R&Dsystems MAB633 ligand receptor 4 antibody TrKB Anti tyrosine Kinase Bantibody GeneTex GTX10B41 TROP2 Anti human trophoblast cell-surfaceSanta Cruz sc80406, sc103908, antibody Biotechnology, Inc. sc103909 Tsg101 Anti tumor susceptibility gene 101 Santa Cruz sc-101254 antibodyTWEAK Anti Tumor necrosis factor like weak United States T9185-01inducer of apoptosis Biological UNC93A Anti unc93a Santa Cruz sc135539,sc135541 VEGFA Anti Vascular endothelial growth factor United StatesV2110-16A antibody Biological

As indicated in Table 46, multiple antibodies to a single targetbiomarker are tested as available and as desired. This allows forassessing vesicle capture using different surface epitopes to determinewhich provide the desired performance.

FIG. 108A shows the fold-change in vesicles identified using severalcapture biomarkers that detected vesicle biomarkers overexpressed in CRCas compared to normal. CRC detection using antibody capture of vesicleswas performed using 128 total samples consisting of 49 normals, 20confounders, and 59 CRC. Confounder samples included those havingrheumatoid arthritis, asthma, diabetes, bladder cell carcinoma, renalcell carcinoma, and chronic or acute diverticulitis. Of the CRC samples,16 were Stage I, 19 were Stage II, and 24 were Stage III. As noted,repeated markers in the list are different antibodies to the sameantigen. FIG. 108A shows discrimination of Normal and CRC samples usingantibodies to multiple biomarkers to capture and detect vesicles.Capture antibodies are shown on the X axis and detection antibodies areshown on the Y axis. Antibodies showing the greatest increase in cancersamples included those to CD66(CEA), A33, EPHA2, TROP2, DR3, UNC93A,NGAL, and MUC17. Table 47 below shows sensitivity and specificityobtained with various capture antibodies:

TABLE 47 CRC Detection using Antibody Capture of Vesicles MarkerSensitivity Specificity DR3 82% 86% STEAP 100% 71% epha2 90% 83% TMEM211100% 84% unc93A 86% 84% A33 100% 75% CD24 98% 77% NGAL 94% 81% EpCam 90%62% MUC17 86% 77% TROP2 96% 80% TETS 86% 80%

FIG. 108B shows results from similar experiments except that the Y-axisrepresents the median fluorescence intensity (MFI) in CRC and normalsamples as indicated by the legend. The experiments as shown in FIG.108B were repeated with a second sample set of 10 CRC samples and 10normal. Results are shown in FIG. 108C. The markers identified as mostoverexpressed between cancers and normal were similar using eithersample set. FIG. 108D shows the ability to distinguish between normalsand CRC using various combinations of the above markers. The plots showMFI on the X and Y axes for the indicated markers. CD24 is used as acolon marker, TROP2 as a cancer marker, and the tetraspanins CD9, CD63and CD81 are general vesicle markers.

The ability to assay the MFI for multiple surface antigens in a singlemultiplexed experiment can be used for discovery of optimal targetbiomarkers. The same techniques can be applied in various settings(e.g., different diseases, different cancers, different targetbiomarkers, diagnosis, prognosis, theranosis, etc.) to identify novelbiomarkers for subsequent assay development.

Example 53 Detection of Colorectal Cancer (CRC) with TMEM211 and CD24

Concentrated microvesicle plasma samples were run on a microsphereplatform as described herein. Antibodies to various surface antigenswere attached to beads and used to capture microvesicles. Severalantibodies showed significant differences between samples derived fromCRC and normal patients. The captured microvesicles were labeled withCD9, CD63, and/or CD81. In this example, vesicles from samples arecaptured using capture antibodies to TMEM211 and/or CD24 (FIGS. 109A-H).Assays are performed according to the methodology outlined in Example52.

CD24 is a glycosyl phosphatidylinositol-anchored protein (Pierres et al,1987; Kay et al, 1990; Alternan et al, 1990) expressed on immature cellsof most, if not all, major hematopoietic lineages, as well as indeveloping neurons (Nedelec et al, 1992; Shirasawa et al, 1993; Rougonet al, 1991) and embryonic intestinal, nasal, salivary gland, renal ratepithelial cells (Shirasawa et al, 1993), regenerating muscle(Figarella-Branger et al, 1993). CD24 is usually absent from cells thathave reached their final differentiation stage. Expression of CD24 isstrongly induced and then repressed again during maturation of T-cellsand B-cells (Allman et al, 1992; Bruce et al, 1981; Crispe and Bevan,1987; Hardy et al, 1991; Husmann et al, 1988; Linton et al, 1989;Symington and Hakamori, 1984; Takei et al, 1981). Erythrocytes are anexception in that they maintain high levels of CD24 expression. CD24 isexpressed also in keratinocytes (Magnaldo and Barrandon, 1996),epidermal Langerhans cells (Enk and Katz, 1994), and dendritic cells(Inaba et al, 1992; Ardavin and Shortman, 1992). CD24 is expressed as amajor surface antigen on small cell lung carcinomas (Jackson et al.1992). It is expressed in a variety of carcinomas (Karran et al, 1995;Akashi et al, 1994; Weber et al, 1995).

Nielsen et al (1997) have generated knock-out mice lacking expression ofCD24. These mice are characterized by a normal development of T-cellsand myeloid cells but show a leaky block in B-cell development with areduction in late pre-B-cells and immature B-cell populations in thebone marrow. Peripheral B-cell numbers are normal and no impairment ofimmune function is detected in these mice in a variety of immunizationand infection models. Erythrocytes from these mice show a highertendency to aggregate, are more susceptible to hypotonic lysis in vitro,and have a shorter life-span in vivo.

Lu et al (2000) have reported that a slight overexpression of CD24 intransgenic mice leads to depletion of B-lymphoid cells in the bonemarrow, which may be caused by increased cell death by apoptosis ofpre-B-cells.

The transmembrane protein 211 (TMEM211) gene encodes a transmembraneprotein. The mRNA has four splice variants, including the following geneand protein sequence:

Protein sequence (SEQ ID NO. 1) 1mllggwllla fnaifllswa vapkglcprr ssvpmpgvga vaatamivgl lifpiglasp 61fikevceass myyggkcrlg wgymtailna vlasllpiis wphttkvqgr tiifssater 121iifvpemnk cDNA sequence (SEQ ID NO. 2) 1ctttgcctgg aaggtctcag ctgtgatgct cctcggaggc tggctcctgt tggccttcaa 61tgcaattttc ctcctgtctt gggctgtggc ccccaaaggg ctgtgcccaa ggagaagcag 121tgttccaatg ccaggggtgc aggcagtggc agctactgcc atgattgtgg gtctgctgat 181tttcccaatc ggccttgcct ccccattcat caaggaagtg tgcgaagcct cctccatgta 241ttatggtggg aagtgccggc tgggttgggg ttacatgact gctatcctca atgcagtcct 301ggccagcctc ctgcccatca tcagctggcc ccacacaacc aaggtccaag ggaggaccat 361catcttctcc agtgccaccg agagaatcat ctttgtgcca gaaatgaaca aataaaaatc 421tcctgggagt agcacaaagg gcacactcca gagttttatg aaatcatcat gtagccaact 481tcaaatccca tctctgctcc ttcttgc

TMEM is well conserved amongst various species. Promoter analysis fortranscription factor binding sites shows that motifs for CdxA proteinsare abundant. Homeobox protein CDX-1 is a protein that in humans isencoded by the CDX1 gene. This gene is a member of the caudal-relatedhomeobox transcription factor gene family. The encoded DNA-bindingprotein regulates intestine-specific gene expression and enterocytedifferentiation. It has been shown to induce expression of theintestinal alkaline phosphatase gene, and inhibit beta-catenin/T-cellfactor transcriptional activity.

Colorectal detection using antibody capture of vesicles was performedusing 147 total samples consisting of 58 normals, 30 confounders, and 59CRC (FIG. 109C). Confounder samples included those having conditions asshown in the table in Table 48.

TABLE 48 Confounder samples for vesicle CRC test Number Confoundersamples Rheumatoid arthritis 3 Diabetes - Type II, Transitional cellcarcinoma of the bladder 2 Rheumatoid arthritis, Marked degenerativearthritis 2 Diabetes, Clear cell renal cell carcinoma. of the kidney 1Diabetes, Infiltrating ductal carcinoma of the breast 1 Diabetes, Renalcell carcinoma 1 Chronic diverticulosis 9 Lung Cancer 11

ROC analysis for the biomarkers TMEM211 and CD24 are depicted in FIG.109A and FIG. 109B, respectively. The sensitivity was 92% and thespecificity 90% in assays with CD24. Using TMEM211 as the captureantibody and detection antibodies CD9, CD63, CD81, the data in Table 49was obtained for detection of CRC in the samples described above:

TABLE 49 CRC Detection using TMEM211 True Positive 59 True Negative 58False Positive 11 False Negative 0 Total 128 Sensitivity Specificity100.00% 84.06%

In a confirmatory follow-on study, TMEM211 was used to detect colorectalcancer in a cohort of 225 patients comprising 76 CRC samples, 80 normalcontrols, and 69 confounder samples. Anti-TMEM211 was used as thecapture antibody and detection antibodies comprised anti-CD9, anti-CD63,and anti-CD81. The confounder samples are shown in Table 50.

TABLE 50 Confounder samples for vesicle CRC test Number Confoundersamples Rheumatoid arthritis 5 Diabetes - Type II, Transitional cellcarcinoma of the bladder 3 Diabetes, Clear cell renal cell carcinoma ofthe kidney 2 Infiltrating ductal carcinoma of the breast 1 Chronicdiverticulosis 9 Lung Cancer 49

Results of the follow on study are shown in FIG. 109D-E. FIG. 109D showsthe mean fluorescence intensity (MFI) of the samples using TMEM211.Results obtained with the indicated threshold (horizontal bar) are shownin Table 51.

TABLE 51 Results obtained with TMEM211 to detect CRC True Positive 73True Negative 124 False Positive 25 False Negative 3 Total Samples 225Sensitivity 96% Specificity 83%

ROC analysis for the same assessed with TMEM211 is depicted in FIG.109E. The AUC was 0.952.

An additional confirmatory study was performed using a patient cohortconsisting of plasma samples from normal controls, Stage I CRC patients,Stage II CRC patients, Stage III CRC patients, and confounders. Asdescribed above, the confounder samples were from patients with cancersother than CRC and other disease, including samples from patients withrheumatoid arthritis; diabetes type II and transitional cell carcinomaof the bladder; diabetes and clear cell renal cell carcinoma of thekidney; diabetes and infiltrating ductal carcinoma of the breast;chronic diverticulosis; and lung cancer. Performance of the plasmamicrovesicle assay for detecting CRC is shown in Table 52.

TABLE 52 Performance of TMEM211 and CD24 to detect CRC TMEM CD24 TMEM &CD24 With confounders True Positive 73 72 72 True Negative 208 205 221False Positive 69 72 56 False Negative 3 4 4 Total 353 353 353Sensitivity 96% 95% 95% Specificity 75% 74% 80% Accuracy 80% 78% 83%With confounding cancers and diverticulitis True Positive 73 72 72 TrueNegative 201 197 212 False Positive 56 60 45 False Negative 3 4 4 Total333 333 333 Sensitivity 96% 95% 95% Specificity 78% 77% 82% Accuracy 82%81% 85% Without confounders True Positive 73 72 72 True Negative 135 129139 False Positive 18 24 14 False Negative 3 4 4 Total 229 229 229Sensitivity 96% 95% 95% Specificity 88% 84% 91% Accuracy 91% 88% 92%

Results for all samples using TMEM211 and CD24 are shown graphically inFIG. 109F. Using a combination of TMEM211 and CD24, the test identified13 of 13 Stage I CRC cancers (100% sensitivity), 22 of 23 Stage II CRCcancers (96% sensitivity) and 37 of 40 Stage III CRC cancers (93%sensitivity). Results for TMEM211 and CD24 to distinguish the variousclasses individually is shown in FIG. 109G and FIG. 109H, respectively.

Example 54 microRNA Overexpression in Colorectal Cancer Cell Lines

TaqMan Low Density Array (TLDA) miRNA cards were used to compareexpression of miRNA in CRC cell lines versus normal vesicles. The miRNAwas collected and analyzed using the TaqMan® MicroRNA Assays and Arrayssystems from Applied Biosystems, Foster City, Calif. Applied BiosystemsTaqMan® Human MicroRNA Arrays were used according to the Megaplex™ PoolsQuick Reference Card protocol supplied by the manufacturer. See Example17.

FIG. 110 illustrates TLDA miRNA card comparison of colorectal cancer(CRC) cell lines versus normal vesicles. The cell lines include LOVO,HT29, SW260, COLO205, HCT116 and RKO. The plot shows a 2-3 fold increasein expression in the CRC cell lines compared to normal controls. ThesemiRNAs were not overexpressed in melanoma cells.

The sequences assayed in FIG. 110 include miR-548c-5p, miR-362-3p,miR-422a, miR-597, miR-429, miR-200a, and miR-200b. Sequences of themiRNAs are shown in Table 53:

TABLE 53 microRNAs Name Sequence miRNA Base Accession SEQ ID NO.hsa-miR-548c-5p aaaaguaauugcgguuuuugcc MIMAT0004806 3 hsa-miR-362-3paacacaccuauucaaggauuca MI0000762 4 hsa-miR-422a acuggacuuagggucagaaggcMIMAT0001339 5 hsa-miR-597 ugugucacucgaugaccacugu MIMAT0003265 6hsa-miR-429 uaauacugucugguaaaaccgu MIMAT0001536 7 hsa-miR-200auaacacugucugguaacgaugu MI0000737 8 hsa-miR-200b uaauacugccugguaaugaugaMI0000342 9

Example 55 microRNA to Detect CRC

MicroRNAs (miRs) were obtained from vesicles isolated from 12 CRCpatients and 4 control patients. The samples were analyzed for two miRs,miR 92 and miR 491, that had been identified as overexpressed in CRCcell lines. FIG. 111A shows that higher levels of these miRs were alsofound in CRC patient samples versus normals. FIG. 111B shows thattogether miR 92 and miR 21 result in improved differentiation of normaland CRC samples. FIG. 111C shows the addition of additional miRs thatcan be multiplexed with miR 92 and miR 21. The figure shows multiplexingof miR 92, miR 21, miR 9 and miR 491 to detect CRC.

Example 56 KRAS Sequencing in CRC Cell Lines and Patient Samples

KRAS RNA was isolated from vesicles derived from CRC cell lines andsequenced. RNA was converted to cDNA prior to sequencing. Sequencing wasperformed on the cell lines listed in Table 54:

TABLE 54 CRC cell lines and KRAS sequence DNA or KRAS GenotypeKRAS Genotype Cell Line Vesicle cDNA Exon 2 Exon 3 Colo 205 Vesicle cDNAWild type (WT) WT Colo 205 DNA WT WT HCT 116 Vesicle cDNA c.13G > GA WTHCT 116 DNA c.13G > GA WT HT29 Vesicle cDNA WT WT Lovo Vesicle cDNAc.13G > GA WT Lovo DNA c.13G > GA WT RKO Vesicle cDNA WT WT SW 620Vesicle cDNA c.12G > T WT

Table 54 and FIG. 112 show that the mutations detected in the genomicDNA from the cell lines was also detected in RNA contained withinvesicles derived from the cell lines. FIG. 112 shows the sequence in HCT116 cells of cDNA derived from vesicle mRNA in (FIG. 112A) and genomicDNA (FIG. 112B).

Twelve CRC patient samples were sequenced for KRAS. As shown in Table55, all were wild type (WT). All patient samples received a DNasetreatment during RNA Extraction. RNA was extracted from isolatedvesicles. All 12 patients amplified for GAPDH demonstrating RNA waspresent in their vesicles.

TABLE 55 CRC patient samples and KRAS sequence KRAS Genotype KRASGenotype Sample Sample Type Stage Exon 2 Exon 3 61473a6 Colon Ca 1 WT WT62454a4 Colon Ca 1 WT WT 110681a4 Colon Ca 1 WT Failed sequencing28836a7 Colon Ca 1 WT Failed sequencing 62025a2 Colon Ca 2a WT WT62015a4 Colon Ca 2a WT WT 110638a3 Colon Ca 2a WT WT 110775a3 Colon Ca2a WT WT 35512a5 Colon Ca 3 WT WT 73231a1 Colon Ca 2a WT WT 85823a3Colon Ca 3b WT WT 23440a7 Colon Ca 3c WT WT 145151A2/3 Normal WT WT139231A3 Normal WT Failed sequencing 145155A4 Normal WT Failedsequencing 145154A4 Normal WT Failed sequencing

In a patient sample wherein the patient was found positive for the KRAS13G>A mutation, the KRAS mutation from the tumor of CRC patient samplescould also be identified in plasma-derived vesicles from the samepatient. FIG. 112 shows the sequence in this patient of cDNA derivedfrom vesicle mRNA in plasma (FIG. 112C) and also genomic DNA derivedfrom a fresh frozen paraffin embedded (FFPE) tumor sample (FIG. 112D).

Example 57 CRC miRs in Vesicle Fractions

In this example, miRNAs found in vesicles of size 50-100 nm (smallvesicles) and size 100-1,000 nm (large vesicles) fractions in serum wascompared.

RNA from three 1 ml colorectal cancer (CRC) patient serum samples wasisolated using the Exomir kit from Bioo Scientific (Austin, Tex.). Thismethod uses a filter to separate the large vesicle and small vesicleportions. The serum was spun in a centrifuge before isolation to removecellular debris.

40 ng of RNA was added to an RT-PCR reaction and the Exiqon miRCURY LNA™Universal RT microRNA PCR Human Panels I and II (Exiqon, Inc, Woburn,Mass.) were used to evaluate the relative expression of 742 miRs. Theresults were normalized and analyzed using GeneSpring GX 11.0 (AgilentTechnologies, Inc., Santa Clara, Calif.) following manufacturer'sprotocol. The measurements for each sample were normalized tointer-plate calibrators and RT-PCR calibrators, a paired t-test was usedto compare the large vesicle-small vesicle paired samples. Statisticalsignificance was determined at an uncorrected p value of 0.05. Five miRswere found to be significantly differentially expressed. See Table 56.

TABLE 56 miR levels in Large and Small Vesicle Populations Isolated fromPaired Samples Regulation in Large versus Small Fold miR Detectorp-value Vesicles Change hsa-miR-376c 0.0067 up 56.4 hsa-miR-652 0.0015up 287.8 hsa-miR-221* 0.049 down 42.1 hsa-miR-215 0.019 down 46.0hsa-miR-324-5p 0.028 up 36.2

A fold change comparison identified 361 miRs that had a greater thantwo-fold difference between large vesicles and small vesicles. EightmiRs were detected in large vesicles but not small vesicles and threemiRs were only detected in small vesicles but not large vesicles. SeeTable 57.

TABLE 57 miR levels in Large Vesicle and Small Vesicle PopulationsIsolated from Paired Samples Detected Only in Large Vesicles DetectedOnly in Small Vesicles hsa-miR-376c hsa-miR-215 hsa-miR-652hsa-miR-582-5p hsa-miR-324-5p hsa-miR-1296 hsa-miR-28-5p hsa-miR-190hsa-miR-590-5p hsa-miR-202 hsa-miR-195

These data reveal that the miRNA content of large vesicles and smallvesicles in a sample is similar but not necessarily identical. Suchdifferences can be used to optimize diagnostic tests.

Example 58 CRC Marker Combinations

In this Example, assays were performed as described in Example 52 above.The sample set comprised 462 samples, 256 were from individuals withbiopsy confirmed CRC and 206 samples were normals (defined for thesepurposes as individuals without CRC). The 256 cancer samples included 12unstaged samples, 57 stage I, 103 stage II, 78 stage III, and six stageIV. Normal samples are age-range matched self declared disease freeindividuals. Antibodies to the indicated vesicle surface antigens MUC1,GPCR 110, TMEM211 and CD24 were attached to beads and used to capturemicrovesicles in the plasma samples. The bead-captured microvesicleswere labeled with PE-labeled CD9, CD63, CD81 and fluorescence of boundvesicles was determined. Fluorescent intensities for the markers wereused to classify the samples as CRC versus normal.

In the Human Gene Ontology (HUGO) database, GPCR 110 is also referred tounder the approved name G protein-coupled receptor 110 or the approvedsymbol GPR110. See www.genenames.org/data/hgnc_data.php?hgnc_id=18990.Two alternative transcripts are identified as REFSEQ proteinsNP_(—)079324.2 and NP_(—)722582.2. The GRP110 gene encodes a cellmembrane protein.

The HUGO approved name for MUC1 is mucin 1, cell surface associated. Seewww.genenames.org/data/hgnc_data.php?hgnc_id=7508. Seven alternativetranscripts are identified as REFSEQ proteins NP_(—)001018016.1,NP_(—)001018017.1, NP_(—)001037855.1, NP_(—)001037856.1,NP_(—)001037857.1, NP_(—)001037858.1, and NP_(—)002447.4. The MUC1 geneis a member of the mucin family and encodes a membrane bound,glycosylated phosphoprotein that plays a role in cell adhesion.

MUC1, GPCR 110, TMEM211 and CD24 were used as capture antibodies fordetecting the microvesicles in the CRC and normal plasma. The capturedvesicles were labeled as described with CD9, CD63, and CD81. Medianfluorescence intensity (MFI) cut off thresholds were determined tooptimally separate cancer patients from normals. If a sample waselevated in a marker, then the sample was considered positive forcolorectal cancer (CRC). Diagnostic performance of each individualmarker is shown in Table 58.

TABLE 58 MFI of microvesicles in plasma samples for CRC versus normalfor individual markers. Muc1 GPCR 110 TMEM211 CD24 True Positive 232 225235 212 True Negative 162 168 151 182 False Positive 44 38 55 26 FalseNegative 24 31 21 46 Total 462 Sensitivity 90.63% 87.89% 91.80% 82.17%Specificity 78.64% 81.55% 73.30% 87.50% Accuracy 85.28% 85.06% 83.55%84.55% MFI Cutoff 390 360 200 600

Detection of CRC using various marker combinations is shown in Tables59-61. In Table 61, a sample was considered positive for colorectalcancer (CRC) if either of the markers within the logical disjunction(i.e., “or”) was positive. As an example, “Muc1 & GPCR & (TMEM or CD24)”is considered positive if Muc1 is positive and GCPR (i.e., GPR110) ispositive, and either of TMEM (i.e., TMEM211) or CD24 is positive. Bycomparing the results of Table 58 with those of Tables 59-61, it isobserved that single markers can provide highly accurate separation ofCRC and normal samples, but that detection of CRC can be improved insome cases using multiple markers.

TABLE 59 MFI of microvesicles in plasma samples for CRC versus normalfor two marker combinations Muc1 Muc1 Muc1 GPCR GPCR TMEM GPCR TMEM CD24TMEM CD24 CD24 True Positive 223 232 211 224 210 212 True Negative 171171 181 174 182 183 False Positive 35 35 25 32 24 25 False Negative 3324 45 32 46 46 Total 462 462 462 462 462 466 Sensitivity 87.11% 90.63%82.42% 87.50% 82.03% 82.17% Specificity 83.01% 83.01% 87.86% 84.47%88.35% 87.98% Accuracy 85.28% 87.23% 84.85% 86.15% 84.85% 84.76%

TABLE 60 MFI of microvesicles in plasma samples for CRC versus normalfor multiple marker combinations Muc1 Muc1 Muc1 GPCR GPCR GPCR TMEM TMEMTMEM CD24 CD24 CD24 All 4 True Positive 223 211 211 211 211 TrueNegative 174 182 182 183 183 False Positive 32 24 24 25 25 FalseNegative 33 45 45 47 47 Total 462 462 462 466 466 Sensitivity 87.11%82.42% 82.42% 81.78% 81.78% Specificity 84.47% 88.35% 88.35% 87.98%87.98% Accuracy 85.93% 85.06% 85.06% 84.55% 84.55%

TABLE 61 MFI of microvesicles in plasma samples for CRC versus normalfor multiple marker combinations Muc1 & TMEM & GPCR & Muc1 & GPCR &(Muc1 or GPCR & Muc1 & TMEM & (GPCR or (Muc1 or GPCR) & (TMEM or (GPCRor (Muc1 or TMEM) & TMEM) & TMEM & CD24) CD24) CD24) CD24 CD24 CD24 TruePositive 223 224 223 211 210 212 True Negative 174 174 174 182 182 183False Positive 32 32 32 24 24 25 False Negative 33 32 33 45 46 46 Total462 462 462 462 462 466 Sensitivity 87.11% 87.50% 87.11% 82.42% 82.03%82.17% Specificity 84.47% 84.47% 84.47% 88.35% 88.35% 87.98% Accuracy85.93% 86.15% 85.93% 85.06% 84.85% 84.76%

FIG. 113 shows a plot in which TMEM211 and MUC1 were used as captureantibodies for detecting the microvesicles in the CRC and normal plasma.The captured vesicles were labeled as described with CD9, CD63, andCD81. Median fluorescence intensity (MFI) cut off thresholds weredetermined to optimally separate cancer patients from normals. If asample was elevated in both markers, then the sample was consideredpositive for colorectal cancer (CRC). Diagnostic performance of eachindividual marker and the combination of TMEM211 and MUC1 is shown inTables 58 and 59 above.

Example 59 Vesicle Biosignatures for Breast Cancer (BCa)

Antibodies to a number of antigens of interest were tethered to beadsand used to capture vesicles in blood samples from 10 subjects withbreast cancer or 10 normals (i.e., no breast cancer) following themethodology of Examples 49-50. Capture antibodies were directed to thevesicle antigens described in this Example. The bead captured vesicleswere detected with fluorescently labeled antibodies against thetetraspanins CD9, CD63 and CD81. The median fluorescence intensity (MFI)of the captured and labeled vesicles was measured using laser detection.

The analysis was perfomed using a panel of capture antibodies. Therewere significant differences in the MFI of detected vesicles betweenbreast cancer and normal plasma when analysis was performed using thefollowing capture antibodies to the following vesicle antigens: CD9,HSP70, Gal3, MIS, EGFR, ER, ICB3, CD63, B7H4, MUC1, DLL4, CD81, ERB3,VEGF, BCA225, BRCA, CA125, CD174, CD24, ERB2, NGAL, GPR30, CYFRA21,CD31, cMET, MUC2 and ERB4.

Follow on experiments were performed using 10 breast cancer samples and10 normal samples and additional capture antibodies. FIG. 114Aillustrates a graph depicting the fold change over normal of theindicated biomarkers expressed in a breast cancer. The markers includefrom left to right CD9, EphA2, EGFR, B7H3, PSMA, PCSA, CD63, STEAP,STEAP, CD81, B7H3, STEAP1, ICAM1 (CD54), PSMA, A33, DR3, CD66e, MFG-8e,EphA2, Hepsin, TMEM211, EphA2, TROP-2, EGFR, Mammoglobin, Hepsin,NPGP/NPFF2, PSCA, 5T4, NGAL, NK-2, EpCam, NGAL, NK-1R, PSMA, 5T4, PAI-1,and CD45. Multiple bars for the same antigen indicate the use ofdifferent capture antibodies that may recognize different epitopes.

FIG. 114B illustrates the level of various biomarkers detected invesicles derived from breast cancer cell lines MCF7, T47D and MDA. T47Dand MDA are metastatic cell lines. Antigens observed in breast cancercell lines include CD9, MIS Rii, ER, CD63, MUC1, HER3, STAT3, VEGFA,BCA, CA125, CD24, EPCAM, and ERB B4.

The ability to assay multiple vesicle biomarkers in a single multiplexedexperiment can be used to create a biosignature for breast cancer andfor discovery of optimal target biomarkers for additional biosignatures.The same techniques can be applied in various settings (e.g., differentdiseases, different cancers, different target biomarkers, diagnosis,prognosis, theranosis, etc.) to identify novel biomarkers for subsequentassay development.

Example 60 Analysis of Breast Cancer (BCa) Vesicles Using MultiplexAssays

In this example, plasma samples from patients with breast cancer (BCa)or without BCa (normals) are analyzed according to the general procedureoutlined in Example 27. Plasma is prepared according to the protocol ofExample 49 and multiplex analysis is performed as in Example 50. Captureantibodies to the vesicle surface antigen proteins in Table 62 were usedto screen for biomarkers that detect BCa.

TABLE 62 BCa Capture Antibodies Antibody Target Antibody Vendor CatalogNumber PGP9.5 clone 3D9 Genway 20-002-35062 CD9 209306 R&D MAB1880 HSP70W27 Biolegend 648002 gal3-b2c10 B2C10 Santa Cruz sc-32790 EGFR EGFR.1 BDBiosciences 555996 iC3b 013III-1.16 Thermo MA1-82814 PSMA LNI-17Biolegend 342502B PCSA 5E10 MILLIPORE MAB4089 CD63 H5C6 BD 556019 MUC1c.Vu4H5 VU4H5 Santa Cruz sc-7313 DLL4 Polyclonal Santa Cruz sc-18639CD81 JS-81 BD 555675 B7-H3 MIH35 BioLegend 135604 HER 3 (ErbB3)Polyclonal US Biological E3451-36A MART-1 0.N.396 US Biological M2410PSA 181811 R&D MAB13442 VEGF A 5J63 US Biological V2110-05D TIMP-1, 4d124D12 Sigma-Aldrich WH0007076M1 GPCR GPR110 Polyclonal GeneTex GTX70591EphA2 (H-77) Polyclonal Santa Cruz sc-10746 MMP9 c.SB15C SB15c NovusNBP1-28617 Biologicals mmp7 Polyclonal Novus NB300-1000 TMEM211 c.c15Polyclonal Santa Cruz sc-86534 UNC93a c.c13 Polyclonal Santa Cruzsc135539 BRCA 1.A.26 US Biological B2708-06 CA125 (MUC16) 8J453 USBiological C0050-01D Mammaglobin Polyclonal Santa Cruz sc-48328 CD174(Lewis y) 8.S.289 US Biological L2056 CD66e CEA (poly) Polyclonal USbiological C1300-08 CD24 c.sn3 SN3 Santa Cruz sc-19585 C-erbB242/c-erbB2-2 BD 610161 CD10 HI10a BD 555373 NGAL c.h130 Polyclonal SantaCruz sc-50350 epcam 158206 R&D MAB9601 CEA 8J503 US Biological C1300-01B(carcinoembryonic Antigen) GPR30 Polyclonal GeneTex GTX100001 CYFRA21-11603 MedixMab 102221 OPN c.lfmb-14 LFMb-14 Santa Cruz sc-73631 ASPH(D01P) Polyclonal Novus H00000444-D01P ASPH (D03) Polyclonal NovusH00000444-D03 ASPH (G-20) Polyclonal Santa Cruz sc-33367 MUC17 c.c19Polyclonal Santa Cruz sc-32602 hVEGFR2 89106 R&D MAB3572 p53 c.do-1 DO-1BioLegend 645702 ASPH (H-300) Polyclonal Santa Cruz sc-66939 MUC2c.h-300 Polyclonal Santa Cruz sc-15334 NCAM 301040 R&D MAB2408 ASPH(A-10) A-10 Santa Cruz sc-271391 ASPH (AB2) Polyclonal Sigma-AldrichAV51357 ASPH Polyclonal Sigma-Aldrich AV51356 ASPH (666-680) PolyclonalSigma-Aldrich A7110 ErbB4 Polyclonal US Biological E3451-40F ASPH(246-260) Polyclonal Sigma-Aldrich A6985 SPB 2Q2279 US BiologicalS8401-02 SPC Polyclonal US Biological U2575-03 CD9 209306 R&D MAB1880MS4A1 5c11 5C11 Sigma WH0000931M1 EphA2 c.20 Polyclonal Santa Cruzsc-924 MIS RII R&D AF4749 HER2 (ErbB2) 191924 R&D MAB1129 ER 5G106 USBiological E3564-89 PR (B) H5344 R&D PP-H5344-00 MRP8 8L802 USBiological M4688-36A CD63 H5C6 BD 556019 B7H4 Polyclonal US BiologicalB0000-35A TGM2 c.2F4 2F4 Sigma-Aldrich WH0007052M10 CD81 JS-81 BD 555675DR3 Polyclonal US Bio D4012-01B STAT 3 Polyclonal US BiologicalS7971-01M MACC-1 (poly) Polyclonal ProSci Inc 5197 TrKB (poly)Polyclonal Novus NB100-92063 biologicals IL 6 Unc 8H12 InvitrogenAHC0762 OPG-13 OPG-13 Biovendor RD182003110-13 IL6R 2G6 SigmaWH0003570M1 EZH2 2C3 ABD serotec MCA4898Z SCRN1 Polyclonal SigmaHPA024517 TWEAK Polyclonal US biological T9185-01 SERPINB3 2F5 SigmaWH0006317M1 CDAC1 1a2 1A2 Sigma WH0081602M1 BCA-225 BRST-1 US BiologicalB0395-10B DR3 Polyclonal US Biological D4012-01B a33 n15 sc33012Polyclonal Santa Cruz sc-33012 NPGP/NPFF2 Polyclonal Santa Cruz sc-46206TIMP1 poly us bio Polyclonal US Biological T5580-08B BDNF us bioPolyclonal US Bio B2700-02D FRT c.f23 F23 Santa Cruz sc-51887 Ferritinheavy chain F31 Santa Cruz sc-51888 seprase R&D 427819 R&D MAB3715p53_Clone do-7 DO-7 BioLegend 645802 ost akm2a1 AKm2A1 Santa Cruzsc-21742 LDH Polyclonal US Bio L1011-12H HSP 8L126 US Bio H1830-94G ostzz09 ZZ09 Santa Cruz sc-80262 p53_Clone BP53-12 BioLegend 628202 BP53-12CXCL12 79014 R&D MAB310 HAP 1.C.1 USBIO H1820-05 CRP 3H109 US BioC7907-05A Gro-alpha Polyclonal GeneTex GTX10376 Tsg 101 c.Y16J Y16JSanta Cruz sc101254 GDF15 LIFESPAN LS-C89472 BIOSC.

As indicated in Table 62, multiple antibodies to a single targetbiomarker are tested as available and as desired. This allows forassessing vesicle capture using different surface epitopes to determinewhich provide the desired performance for detecting BCa.

Example 61 Plasma-Derived Microvesicles in Breast Cancer

Circulating microvesicles play an important role in several biologicprocesses, including angiogenesis and immune modulation. This Examplelooked at the level of specific microvesicles subpopulations that arealtered in patients with a disease, specifically breast cancer.Monitoring microvesicle subpopulations will help identify importantbiologic processes associated with the progression of cancers and otherdiseases.

To identify cancer associated microvesicles, microvesicles in patientsamples were compared between a cohort of patients with advanced breastcancer versus normal controls without breast cancer. Microvesicles (MVs)in plasma samples from the cohort were concentrated, stained withfluorochrome conjugated antibodies and analyzed using flow cytometry.Tumor-specific, leukocyte-specific and stromal cell-specific antibodieswere used to identify and characterize these subtypes of microvesiclesin the plasma samples. These tissue-specific antibodies were paired withprocess-specific markers such as DLL4 and VEGFR2 for angiogenicmicrovesicles, CTLA4 and FasL for immunosuppressive microvesicles, andCD80 and CD83 for immunostimulatory microvesicles. The vesicles weredetected using labeled capture antibodies to the markers to label thevesicles, then flow cytometry was used to detect the labeled vesicles asdescribed herein.

Circulating microvesicles were compared between breast cancer patientsand healthy controls. Distinct and informative subpopulations wereidentified and quantified by probing antigens associated with thevesicles. Immunosuppressive microvesicles were elevated in the cancerpatients (68% vs. 44% of CD45′ MVs co-expressed CTLA4). Additionally,angiogenic MVs were elevated in the cancer patients' plasma, with 44% ofcirculating microvesicles co-expressing DLL4 and CD31 compared with 2%of vesicles displaying these markers in the normal controls. Theseresults show that the plasma from normal individuals contains decreasednumbers of angiogenic, immunosuppressive and cancer-associatedmicrovesicles as compared to the plasma of advanced cancer patients.Circulating microvesicles can provide a simple and reliable tool toobtain important information about malignant and cancer progressionprocesses occurring in a patient without the need for a biopsy orstandard pathology evaluation such as immunohistochemistry.

Example 62 Vesicle Biosignatures for Lung Cancer (LCa)

Antibodies to a number of antigens of interest were tethered to beadsand used to capture vesicles in plasma samples from subjects with lungcancer, normal controls (i.e., no lung cancer), or other diseases usingmethodology as outlined in Examples 49-50. Capture antibodies weredirected to the vesicle antigens described in this Example. The beadcaptured vesicles were detected with fluorescently labeled antibodiesagainst the tetraspanins CD9, CD63 and CD81. The median fluorescenceintensity (MFI) of the captured and labeled vesicles was measured usinglaser detection.

In a first set of experiments, vesicles were detected following themethodology above in plasma samples from 10 normal control samples, 10non-lung cancer samples, and 10 lung cancer samples as shown in Table63.

TABLE 63 Samples Diagnosis Stage Normal Control - male NA NormalControl - male NA Normal Control - male NA Normal Control - male NANormal Control - male NA Normal Control - Female NA Normal Control -Female NA Normal Control - Female NA Normal Control - Female NA NormalControl - Female NA Endometrioid adenocarcinoma Unstaged Endometrioidadenocarcinoma Unstaged Infiltrating ductal carcinoma of the breastUnstaged Metastatic carcinoma of lymph node(from breast) UnstagedMetastatic carcinoma of lymph node(from breast) Unstaged Renal cellcarcinoma of the kidney Unstaged Renal cell carcinoma of the kidneyUnstaged Renal cell carcinoma of the kidney Unstaged Sclerosing stromaltumor of the ovary Unstaged Serous papillary cystadenoma of the ovaryUnstaged Bronchioloalveolar carcinoma of the lung IA Large cellcarcinoma of the lung IIA Squamous cell carcinoma of the lung IIASquamous cell carcinoma of the lung IA Squamous cell carcinoma of thelung IIA Tubular adenocarcinoma of the lung IB Large cell carcinoma ofthe lung IIB Adenocarcinoma of the lung IIB Adenocarcinoma of the lungIIIA Adenocarcinoma of the lung IB

Vesicles in the samples were captured using antibodies to the antigenslisted in FIG. 115A and FIG. 115B using capture antibodies bound tobeads. From left to right, the antigens in the figures are: SPB, SPC,TFF3, PGP9.5, CD9, MS4A1, NDUFB7, Cal3, iC3b, CD63, MUC1, TGM2, CD81,B7H3, DR3, MACC1, TrkB, TIMP1, GPCR (GPR110), MMP9, MMP7, TMEM211,TWEAK, CDADC1, UNC93, APC, A33, CD66e, TIMP1, CD24, ErbB2, CD10, BDNF,Ferritin, Ferritin, Seprase, NGAL, EpCam, ErbB2, Osteopontin (OPN), LDH,OPN, HSP70, OPN, OPN, OPN, OPN, MUC2, NCAM, CXCL12, Haptoglobin (HAP),CRP, and Gro-alpha. Different capture antibodies are used where the sameantigen appears multiple times, e.g., Erbb2, Ferritin and Osteopontin.The different antibodies may recognize different epitopes of the samebiomarker.

The captured vesicles were detected using fluorescently labeledantibodies to CD9, CD63 and CD81. The detected median fluorescencelevels (MFI) are shown on the Y-axis in FIG. 115B. Ratios of thefluorescence in normals versus lung cancer samples, or normals versusnon-lung cancer samples, are shown in FIG. 115A. FIG. 115C shows the MFIof EPHA2 (i), CD24 (ii), EGFR (iii), and CEA (iv) in samples from lungcancer patients and normal controls.

Concentrated microvesicle plasma samples from lung cancer and normalpatients were collected and analyzed as in Examples 49-50. 69 patientswere screened for 31 capture antibodies to vesicle surface antigens.FIG. 115D presents a graph of mean fluorescence intensity (MFI) on the Yaxis for lung cancer and normal samples, with capture antibodiesindicated along the X axis. The captured vesicles were detected usingfluorescently labeled antibodies to CD9, CD63 and CD81. From left toright, the antigens in the figures are: SPB, SPC, NSE, PGP9.5, CD9,P2RX7, NDUFB7, NSE, Gal3, Osteopontin, CHI3L1, EGFR, B7H3, iC3b, MUC1,Mesothelin, SPA, TPA, PCSA, CD63, AQP5, DLL4, CD81, DR3, PSMA, GPCR 110(GPR110), EPHA2, CEACAM, PTP, CABYR, TMEM211, ADAM28, UNC93a, A33, CD24,CD10, NGAL, EpCam, MUC17, TROP2 and MUC2. Antigens most able todistinguish between lung cancer and normal samples include SPB, SPC,PSP9.5, NDUFB7, Gal3, iC3b, MUC1, GPCR 110, CABYR and MUC17.

In another set of related experiments, the levels of a separate butoverlapping panel of vesicle surface biomarkers was assessed in 115 lungand 78 normals samples. Of the lung cancer samples, there were 35 StageI, 53 Stage II, and 27 Stage III lung cancers. As above, vesicles werecaptured in plasma samples from the cohort using capture antibodies tothe indicated surface antigens, and the captured vesicles were detectedwith labeled detection antibodies to CD9, CD63 and CD81. Results areshown in Table 64 and FIG. 115E. From left to right, the antigens in theFIG. 115E are: CD9, CD63, CD81, B7H3, PRO GRP, CYTO 18, FTH1, TGM2,CENPH, ANNEXIN I, ANNEXIN V, ERB2, EGFR, CRP, VEGF, CYTO 19, CCL2,Osteopontin (OST19), Osteopontin (OST22), BTUB, CD45, TIMP, NACC1, MMP9,BRCA1, P27, NSE, M2PK, HCG, MUC1, CEA, CEACAM, CYTO 7, EPCAM, MS4A1,MUC1, MUC2, PGP9, SPA, SPA, SPD, P53, GPCR (GPR110), SFTPC, UNCR2, NSE,INGA3, INTG b4, MMP1, PNT, RACK1, NAP2, HLA, BMP2, PTH1R, PAN ADH, NCAM,CD151, CKS1, FSHR, HIF, KRAS, LAMP2, SNAIL, TRIM29, TSPAN1, TWIST1, ASPHand AURKB. Table 64 ranks the markers by accuracy for differentiatingbetween cancer and non-cancer samples. As indicated in the table, notall markers were run on all samples due to sample quantity and the like.

TABLE 64 Marker Panel Results for Lung Cancer Sample True True FalseFalse Marker Size Sensitivity Specificity Accuracy Positive NegativePositive Negative NSE 117 57.38 96.43 76.07 35 54 2 26 TRIM29 46 58.62100 73.91 17 17 0 12 CD63 76 74.07 68.18 72.37 40 15 7 14 CD151 47 7076.47 72.34 21 13 4 9 ASPH 47 60 94.12 72.34 18 16 1 12 LAMP2 47 56.67100 72.34 17 17 0 13 TSPAN1 47 56.67 100 72.34 17 17 0 13 SNAIL 46 58.6294.12 71.74 17 16 1 12 CD45 164 50.55 95.89 70.73 46 70 3 45 CKS1 4753.33 100 70.21 16 17 0 14 NSE 77 43.9 100 70.13 18 36 0 23 FSHR 4651.72 100 69.57 15 17 0 14 OPN 193 54.78 91.03 69.43 63 71 7 52 FTH1 19352.17 94.87 69.43 60 74 4 55 PGP9 193 51.3 96.15 69.43 59 75 3 56ANNEXIN 1 193 50.43 97.44 69.43 58 76 2 57 SPD 193 48.7 98.72 68.91 5677 1 59 CD81 112 49.18 92.16 68.75 30 47 4 31 EPCAM 188 52.17 94.5268.62 60 69 4 55 PTH1R 95 55.56 93.75 68.42 35 30 2 28 CEA 193 47.8398.72 68.39 55 77 1 60 CYTO 7 193 47.83 98.72 68.39 55 77 1 60 CCL2 16442.86 100 68.29 39 73 0 52 SPA 192 47.37 98.72 68.23 54 77 1 60 KRAS 4750 100 68.09 15 17 0 15 TWIST1 47 50 100 68.09 15 17 0 15 AURKB 47 50100 68.09 15 17 0 15 MMP9 191 47.79 97.44 68.06 54 76 0 59 P27 169 56.3186.36 68.05 58 57 9 45 MMP1 172 49.04 97.06 68.02 51 66 2 53 HLA 9653.12 96.88 67.71 34 31 1 30 HIF 46 48.28 100 67.39 14 17 0 15 CEACAM193 51.3 91.03 67.36 59 71 7 56 CENPH 193 46.09 98.72 67.36 53 77 1 62BTUB 193 46.09 98.72 67.36 53 77 1 62 INTO b4 172 45.19 100 66.86 47 680 57 EGFR 193 46.09 97.44 66.84 53 76 2 62 NACC1 193 45.22 98.72 66.8452 77 1 63 CYTO 18 193 44.35 100 66.84 51 78 0 64 NAP2 96 50 100 66.6732 32 0 32 CYTO 19 192 45.61 97.44 66.67 52 76 0 62 ANNEXIN V 192 44.7497.44 66.15 51 76 1 63 TGM2 193 45.22 96.15 65.8 52 75 1 63 ERB2 19343.48 98.72 65.8 50 77 1 65 BRCA1 193 43.48 98.72 65.8 50 77 1 65 B7H3146 41.18 100 65.75 35 61 0 50 SFTPC 172 43.27 100 65.7 45 68 0 59 PNT172 43.27 100 65.7 45 68 0 59 NCAM 96 48.44 100 65.62 31 32 0 33 MS4A1192 42.98 98.72 65.62 49 77 1 65 P53 173 42.86 100 65.32 45 68 0 60INGA3 173 42.86 100 65.32 45 68 0 60 MUC2 193 46.09 93.59 65.28 53 73 562 SPA 193 43.48 97.44 65.28 50 76 2 65 OPN 193 42.61 98.72 65.28 49 771 66 CD63 112 45.9 88.24 65.18 28 45 6 33 CD9 112 36.07 100 65.18 22 510 39 MUC1 192 41.23 100 65.1 47 78 0 67 UNCR3 173 42.86 98.53 64.74 4567 1 60 PAN ADH 96 48.44 96.88 64.58 31 31 1 33 HCG 96 46.88 100 64.5830 32 0 34 TIMP 193 41.74 97.44 64.25 48 76 2 67 PSMA 103 41.27 10064.08 26 40 0 37 GPCR 173 40 100 63.58 42 68 0 63 PvACKl 96 45.31 10063.54 29 32 0 35 PCSA 167 40.59 98.48 63.47 41 65 1 60 VEGF 193 37.39100 62.69 43 78 0 72 BMP2 96 45.31 96.88 62.5 29 31 1 35 CD81 76 5090.91 61.84 27 20 0 27 CRP 193 38.26 94.87 61.14 44 74 4 71 PRO GRP 19333.04 98.72 59.59 38 77 1 77 B7H3 76 44.44 95.45 59.21 24 21 1 30 MUC192 33.33 100 56.52 20 32 0 40 M2PK 188 27.93 94.81 55.32 31 73 4 80 CD976 38.89 90.91 53.95 21 20 2 33 PCSA 29 62.5 0 51.72 15 0 5 9 PSMA 7624.07 95.45 44.74 13 21 1 41

The ability to assay multiple vesicle biomarkers in a single multiplexedexperiment can be used to create a biosignature for lung cancer and fordiscovery of optimal target biomarkers for additional biosignatures. Thesame techniques can be applied in various settings (e.g., differentdiseases, different cancers, different target biomarkers, diagnosis,prognosis, theranosis, etc.) to identify novel biomarkers for subsequentassay development.

Example 63 Biosignature on Circulating Microvesicles as Tool forDetecting Lung Cancer

Circulating microvesicles (cMV) are cell derived, membrane-boundstructures that are abundantly present in the blood. Tumor cells producelarge quantities of cMV, and their production has been shown tocorrelate with tumor invasiveness and resistance to therapy. In thisexample, the protein composition of cMV in patients with non-small celllung cancer (NSCLC) was analyzed. Using a decision tree, a bio-signaturethat can predict tumor presence was developed.

cMV that was isolated from blood derived from patients with NSCLC wascompared to cMV in similar samples from control patients to ascertainthe presence of biomarkers indicative of cancer. Antibodies coupled tofluorescently labeled beads were used to detect the presence of cMV inthe samples, using methodology as described herein.

Using a multiplexing analysis and a decision tree, we have optimized thethreshold signal to an optimal level able to segregate the twopopulations. From an initial cohort of 111 patients with NSCLC andcontrols, using a panel of 63 specific biomarkers, we developed an assaywith high specificity and sensitivity. This assay is based on analgorithm derived from a decision three that includes use of fourcapture antibodies to the following vesicle biomarkers: one general cMVmarker, CD81, and three markers for lung cancer, Surfactant Protein D(SPD), Surfactant Protein A (SP-A) and Osteopontin (OPN). Using thecapture antibodies coupled on beads and detection using anti-tetraspanindetection antibodies and laser based detectors as described herein,fluorescence intensities stemming from cMV bound to labeled beads weremeasured in blood samples from a cohort of 40 patients affected by earlystage of lung cancer and 25 controls without lung cancer. The resultingmean fluorescence intensity (MFI) values were run through an algorithmto discriminate cancer. A tree view of the algorithm is presented inFIG. 116. The numbers between each marker indicate the MFI threshold forthat step, which can be adjusted to favor sensitivity or specificity asdescribed. An MFI above 917 for SPD is indicated as positive for cancer.If the MFI for SPD is ≦917, the MFI for CD81 is next considered. An MFIless than 627 for CD81 is indicated as positive for cancer. If the MFIfor CD81 is ≧627, the MFI for SP-A is next considered. An MFI less than375 for SP-A is indicated as negative for cancer. If the MFI for SP-A is≧375, the MFI for OPN is next considered. An MFI less than 80 for OPN isindicated as positive for cancer. An MFI ≧80 for OPN is indicated asnegative for cancer.

Results of the analysis using the decision tree of FIG. 116 are shown inTable 65. As shown in the table, the analysis identified lung cancerpositive samples with a sensitivity of 93%, specificity of 92% andaccuracy of 92%.

TABLE 65 Circulating Microvesicle Detection of Lung Cancer DescriptionPatients True Positive 37 True Negative 23 False Positives  2 FalseNegatives  3 Total 65 Sensitivity 93% Specificity 92% Accuracy 92%

Example 64 Circulating Vesicles Compared to Circulating Tumor Cells

Circulating Tumor Cells (CTCs) have been used to monitor diseaseprogression in patients with different types of metastatic cancer.However, only 50% of metastatic breast, 57% of metastatic prostate, and18% of metastatic colon cancer blood specimens have adequate levels ofCTCs for clinical laboratory analysis. Levels of vesicles can correlatewith tumor progression.

Methods:

Vesicles from 1 ml of plasma were isolated by ultracentrifugation. CD81antibodies were used to capture and measure the vesicle level of breastcancer samples (n=14) and healthy controls (n=4). CTCs were measured forall samples using the Cell Search CTC test protocol. Subsequently, EpCampositive vesicles were captured from metastatic breast (n=10), prostate(n=2), and colon cancer (n=3) samples, and compared to healthy controls(n=7). RNA was extracted from these EpCam positive vesicles andmicroRNA-21 (miR-21) expression was quantified by qRT-PCR.

Results:

Eleven of the 14 samples (78.6%) had CD-81 specific vesicle levelssignificantly above the level found in the 4 healthy samples (p=0.002).See FIG. 117A. Only 7 of the 14 (50%) specimens analyzed had more than 5CTCs, the clinical threshold for metastatic breast cancer. Three cancersamples had CD-81 measured vesicle levels below the average of normalsamples, one of these had >5 CTCs. miR-21 analysis of 15 additionalmetastatic cancer specimens, 5 of which had >5 CTCs, found miR-21averaged 4.2×10⁶, 4.82×10⁶ and 5.05×10⁶ copies in the breast, prostate,and colon cancer samples respectively. See FIG. 117B. Conversely, theplasma specimens from healthy donors collected in EDTA tubes averaged1.8×10⁴ copies of miR21. See FIG. 117B.

Conclusions:

Vesicle analysis from plasma samples offer an opportunity to monitor andtrack disease, in some case better than CTC analysis. Tumor-derivedvesicles provide the ability to characterize tumor of origin miRcontent, which demonstrates additional opportunities for tumor-specificvesicle-based biomarker analysis from a blood sample.

Example 65 Depletion of Vesicles from Plasma

Blood-based cancer diagnostic methods must filter through the vast arrayof biological molecules to select those few that are informative about aparticular disease type from a specific organ. This presents manychallenges especially when there is only a small amount of cellularmaterial released into the blood stream. One strategy to overcome thisobstacle is the selection and interrogation of circulating vesicles tolearn about particular systems in the body. Vesicles comprise lipidbilayer encapsulated bodies such apoptotic bodies, blebs, exosomes,microvesicles and other biological entities as described herein. SeeTable 2 and related discussion. Microvesicles provide a rich source ofinformation and are secreted by most cell types. Endothelial andleukocyte derived circulating microvesicles represent a majority of thecirculating microvesicles present in the blood. This Example useddepletion of these more common circulating microvesicles to allow forthe enrichment and analysis of rarer subpopulations of microvesicles.

Magnetic beads were conjugated with CD31 and CD45 using methodsdescribed herein. The beads were incubated with human plasma from abreast cancer patient in order to deplete endothelial and leukocytederived circulating microvesicles from the sample. The remainingmicrovesicles were characterized with a multiplexed immune basedplatform using capture antibodies to 20 different antigenssimultaneously, according to methods described herein. See Examples49-50. Some highly associated endothelial markers, e.g., DLL4, weresignificantly depleted along with CD31, while more general microvesiclemarkers, e.g., CD9, had significant populations remaining afterdepletion. See FIG. 118. These data indicate that specific populationsof vesicles can be depleted from a patient sample. Similar trends wereobserved using magnetic beads to CD45 to deplete vesicles from thepatient sample.

Example 66 Tissue Factor as a Vesicle Cancer Marker

Tissue factor is a blood clot-related protein whose expression has beennoted in association with cancer. There are several biologic processesrelated to tumorigenesis or cancer progression that is tied to TFexpression. These processes include angiogenesis, cancer cell invasion,immune evasion and circulating tumor cell survival. The fibrin clot thatforms with TF expression coats cancer cells providing a protectivecoating for these cells. It is known that circulating TF is increased inthe serum of cancer patients. Pathologic fibrotic events such asthromboembolism and stroke are major causes of cancer-associated deathsin patients and the existence of TF-expressing circulating microvesicles(cMVs).

FIG. 119 illustrates detection of Tissue Factor (TF) in vesicles from 10normal (non-cancer) plasma samples, eight breast cancer (BCa) plasmasamples and two prostate cancer (PCa) plasma samples. Vesicles in plasmasamples were captured with anti-Tissue Factor antibodies tethered tomicrospheres as described herein. See Examples 49-50 for generalmethodology. The captured vesicles were detected with labeled antibodiesto tetraspanins CD9, CD63 and CD81. The figure shows the medianfluorescence intensity (MFI) observed by laser detection. The MFI of theBCa and PCa samples was consistently greater than the normal samples.The detection of Tissue Factor in diverse cancers indicates that TF canbe used as a cancer vesicle marker.

Example 67 Selecting a Candidate Treatment for a Cancer

The methods of the invention can be used to identify a biosignature fortheranosing a cancer. The biosignature can include any number of usefulbiomarkers, which can be assessed as described herein. The biosignaturecan be determined in a sample of bodily fluid, prefereably a bloodsample, such as plasma or serum. Vesicles are obtained from sample ofbodily fluid from a patient with a cancer using methodology presentedherein. See Examples 49-50 for general methodology. For determining abiosignature for different settings, the appropriate biomarkers toinclude in the biosignature can be discovered as described above. See,e.g., section on Biosignature Discovery. The vesicles can be isolated,captured and/or assessed for surface antigens using a binding agentbound to a microsphere, such as described in Examples 48-50. Thevesicles can also be isolated, captured and/or assessed for surfaceantigens using an array as in Examples 35-36, or FACS as in Example 26.Immunoassay techniques can also be used to capture vesicles. Biomarkerpayload within the isolated/captured vesicles can be analyzed asdesired. The vesicles can be assessed for size using laser detectiontechniques.

The biosignature can further comprise additional biomarkers, such asmicroRNA. MicroRNA can be assessed directly from a bodily fluid or canbe first isolated from a vesicle population. See, e.g., Example 12(obtaining serum); Example 13 (RNA isolation from serum or plasma);Example 16 (extracting microRNA from vesicles). The microRNA can beassessed using RT-PCR (see Examples 14-15) and/or using array analysis(see Example 17). The microRNA can be analyzed using microfluidics toperform nucleic acid amplification.

The methods of identifying a biosignature can be performed in a singleassay. For example, a number of biomarkers can be assessed using amultiplexed approach. In addition, some of the biomarkers can beassessed in a single assay while one or more other biomarkers areassessed in a different assay, which can also be a multiplexed assay. Asan example, multiple vesicle surface biomarkers can be assessed in afirst multiplex assay, and multiple microRNAs can be assessed in asecond multiplex assay. The results of the first and second multiplexassays can be combined to identify a biosignature comprising the vesiclesurface biomarkers and the microRNAs.

The biosignature can comprise any useful biomarker, including withoutlimitation those presented herein in the context of various diseases anddisorders, including without limitation markers for prostate cancer inExamples 8, 11, 28-42, 45-47 and 51; markers for colorectal cancer inExamples 9 and 52-58; markers for breast cancer in Examples 59-61 andmarkers for lung cancer in Examples 62-63.

Example 68 Drug Associated Targets

The cancer is theranosed by identifying a biosignature including drugassociated targets. An advantage of this approach is that thesensitivity of the cancer to a candidate therapeutic can be determinedwithout regard to the origin of the cancer. Rather, the molecularprofile of the tumor itself provides a guide to therapeutic agentselection. A panel of antibodies or aptamers are used to assess avesicle population for the presence or level of ABCC1, ABCG2, ACE2, ADA,ADH1C, ADH4, AGT, AR, AREG, ASNS, BCL2, BCRP, BDCA1, beta III tubulin,BIRC5, B-RAF, BRCA1, BRCA2, CA2, caveolin, CD20, CD25, CD33, CD52, CDA,CDKN2A, CDKN1A, CDKN1B, CDK2, CDW52, CES2, CK 14, CK 17, CK 5/6, c-KIT,c-Met, c-Myc, COX-2, Cyclin D1, DCK, DHFR, DNMT1, DNMT3A, DNMT3B,E-Cadherin, ECGF1, EGFR, EML4-ALK fusion, EPHA2, Epiregulin, ER, ERBR2,ERCC1, ERCC3, EREG, ESR1, FLT1, folate receptor, FOLR1, FOLR2, FSHB,FSHPRH1, FSHR, FYN, GART, GNRH1, GNRHR1, GSTP1, HCK, HDAC1, hENT-1,Her2/Neu, HGF, HIF1A, HIG1, HSP90, HSP90AA1, HSPCA, IGF-1R, IGFRBP,IGFRBP3, IGFRBP4, IGFRBP5, IL13RA1, IL2RA, KDR, Ki67, KIT, K-RAS, LCK,LTB, Lymphotoxin Beta Receptor, LYN, MET, MGMT, MLH1, MMR, MRP1, MS4A1,MSH2, MSH5, Myc, NFKB1, NFKB2, NFKBIA, ODC1, OGFR, p16, p21, p27, p53,p95, PARP-1, PDGFC, PDGFR, PDGFRA, PDGFRB, PGP, PGR, PI3K, POLA, POLA1,PPARG, PPARGC1, PR, PTEN, PTGS2, RAF1, RARA, RRM1, RRM2, RRM2B, RXRB,RXRG, SIK2, SPARC, SRC, SSTR1, SSTR2, SSTR3, SSTR4, SSTR5, Survivin,TK1, TLE3, TNF, TOP1, TOP2A, TOP2B, TS, TXN, TXNRD1, TYMS, VDR, VEGF,VEGFA, VEGFC, VHL, YES1, ZAP70. The antibodies or aptamers can be boundto microspheres as in Examples 48-50 or arrayed as in Examples 35-36.These markers are known to play a role in the efficacy of variouschemotherapeutic agents against proliferative diseases. Accordingly, themarkers can be assessed to select a candidate treatment for the cancerindependent of the origin or type of cancer, although the treatingphysician can take into account any other relevant information whenselecting the candidate treatment, e.g., patient history, priortreatments, other testing results, cancer characteristics (e.g., stage,origin), physician experience, and the like.

The presence or level of each marker is compared to the presence orlevel of the same markers observed in a group of reference sampleswithout the cancer. Biomarkers that are overexpressed or underexpressedin the patient sample compared to the reference samples are identified.A list is assembled of candidate therapeutic agents are that known to beeffective against cancers that overexpress or underexpress thebiomarkers identified using drug-target association rules as presentedin Tables 9-11, and U.S. patent application Ser. No. 12/658,770, filedFeb. 12, 2010; International PCT Patent Application PCT/US2010/000407,filed Feb. 11, 2010; International PCT Patent ApplicationPCT/US2010/54366, filed Oct. 27, 2010; and U.S. Provisional PatentApplication 61/427,788, filed Dec. 28, 2010; all of which applicationsare incorporated by reference herein in their entirety. See, e.g.,“Table 4: Rules Summary for Treatment Selection” of PCT/US2010/54366.The treating physician is presented a report comprising the expressionlevels of the biomarkers assessed and the list of drug indications. Thephysician uses the report to aid in selection of a candidate treatment.

Example 69 Monitoring Treatment Efficacy of Prostate Cancer

Methods for detecting a vesicle biosignature for prostate cancer aredescribed in Examples 29-32. Vesicles are detected in a blood samplefrom a patient. The biosignature is determined by detecting the presenceof the following vesicle surface antigens in the sample:

a. General Vesicle (MV) markers: CD9, CD81, and CD63

b. Prostate MV markers: PCSA, PSMA

c. Cancer-Associated MV markers: B7H3, optionally EpCam

The biosignature is used to monitor an efficacy of a treatment for theprostate cancer. A patient is identified with a suspicious serum PSAlevel (e.g., serum PSA >4.0 ng/ml) and/or a suspicious digital rectalexamination (DRE). The vesicle biosignature is determined for thepatient, and the results are found to be positive for prostate cancer.The treating physician determines whether to treat the prostate cancerwith a therapeutic agent, hormone therapy, or a surgery (prostatectomy).After treatment, the vesicle biosignature is again determined for thepatient. A positive result for cancer indicates a negative patientresponse to the treatment and that further treatment is required. Anegative result indicates a positive patient response to the treatmentand that further treatment may not be necessary.

Alternate biosignatures for prostate cancer can also be used, includingwithout limitation those presented in Examples 8, 11, 28-42, 45-47 or51. Similar methodology is used to monitor treatment efficacy of otherdiseases and disorders. For example, colorectal cancer treatment can bemonitored using a biosignature as described in Examples 9 or 52-58,breast cancer treatment can be monitored using a biosignature asdescribed in Examples 59-61, and lung cancer treatment can be monitoredusing a biosignature as described in Examples 62-63. Biosignatures forthese cancers and other diseases and disorders as presented herein canalso be used to monitor treatment efficacy.

Example 70 TMEM211 Epitope Mapping

A rabbit polyclonal antibody was used to probe a TMEM211 peptide libraryin order to identify the epitope of TMEM211 recognized on the surface ofvesicles. Peptides were 15 amino acids in length and overlapped by 12amino acids. Cysteines were replaced with serine residues. All peptideswere biotinylated on the N-terminus. Peptide sequences are shown inTable 66.

TABLE 66 TMEM211 overlapping peptide library Peptide sequence SEQ ID NO.MLLGGWLLLAFNAIF 10 GGWLLLAFNAIFLLS 11 LLLAFNAIFLLSWAV 12 AFNAIFLLSWAVAPK13 AIFLLSWAVAPKGLS 14 LLSWAVAPKGLSPRR 15 WAVAPKGLSPRRSSV 16APKGLSPRRSSVPMP 17 GLSPRRSSVPMPGVQ 18 PRRSSVPMPGVQAVA 19 SSVPMPGVQAVAATA20 PMPGVQAVAATAMIV 21 GVQAVAATAMIVGLL 22 AVAATAMIVGLLIFP 23ATAMIVGLLIFPIGL 24 MIVGLLIFPIGLASP 25 GLLIFPIGLASPFIK 26 IFPIGLASPFIKEVS27 IGLASPFIKEVSEAS 28 ASPFIKEVSEASSMY 29 FIKEVSEASSMYYGG 30EVSEASSMYYGGKSR 31 EASSMYYGGKSRLGW 32 SMYYGGKSRLGWGYM 33 YGGKSRLGWGYMTAI34 KSRLGWGYMTAILNA 35 LGWGYMTAILNAVLA 36 GYMTAILNAVLASLL 37TAILNAVLASLLPII 38 LNAVLASLLPIISWP 39 VLASLLPIISWPHTT 40 SLLPIISWPHTTKVQ41 PIISWPHTTKVQGRT 42 SWPHTTKVQGRTIIF 43 HTTKVQGRTIIFSSA 44KVQGRTIIFSSATER 45 GRTIIFSSATERIIF 46 IIFSSATERIIFVPE 47 SSATERIIFVPEMNK48

Biotinylated peptides were captured in streptavidin coated wells of a96-well microtiter plate. The plate also contained wells controlpeptides known to be recognized by a control antibody. The plate wasincubated with the primary anti-TMEM211 rabbit polyclonal antibody at0.3 μg/ml or a rabbit polyclonal control antibody at 0.3 μg/ml. Theplates were washed and incubated with a secondary antibody consisting ofgoat anti-rabbit IgG conjugated to horseradish peroxidase (HRP). Anenzymatic colorimetric reaction was performed to detect bound secondaryantibody using standard methodology well known in the art. Afterreaction termination, each well of the microtiter plate was read with aspectrometer at 450 nm wavelength. Experiments were performed intriplicate. Positive readings indicated the primary anti-TMEM211 rabbitpolyclonal antibody or control antibody, as applicable, bound to thetethered peptides.

FIGS. 120A-C show the results of the experiments. In the figures,positions 1-12 (front row, from left to right) correspond to SEQ ID NOs:10-21, positions 13-24 (second row from front, from left to right)correspond to SEQ ID NOs: 22-33, positions 25-36 (third row from front,from left to right) correspond to SEQ ID NOs: 34-45, and positions 37-39(back row, from left to right) correspond to SEQ ID NOs: 46-48.Positions 40 and 41 are the positive control and positions 42 and 43 arenegative controls.

FIG. 120A shows results of incubation of the plate with the anti-TMEM211rabbit polyclonal antibody and goat anti-rabbit HRP secondary antibody.In the figure, peptides at positions 30-33 gave a clear signal. Thesepositions correspond to SEQ ID NOs: 39-42. FIG. 120B shows results ofincubation of the plate with the control rabbit polyclonal antibody andgoat anti-rabbit HRP secondary antibody. The positive controls gave astrong signal. FIG. 120C shows results of incubation of the plate withthe anti-TMEM211 rabbit polyclonal antibody and goat anti-mouse IgG HRPsecondary antibody. Because the secondary antibody recognizes mouse IgG,this experiment also serves as a control. Only the positive controlsgave a signal.

According to these experiments, a series of overlapping peptides wereidentified that are recognized by the anti-TMEM antibody, correspondingto SEQ ID NOs: 39-42. These peptides are equivalent to the N-terminalsequence “LNAVLASLLPIISWPHTTKVQGRT” (SEQ ID NO: 49). As shown in Table67, the common epitope contained in the four overlapping peptides isPIISWP (SEQ ID NO: 50). This indicates that vesicles can be identifiedusing an antibody that recognizes the epitope PIISWP (SEQ ID NO: 50).

TABLE 67 Anti-TMEM211 common epitope SEQ ID NO. Peptide sequence 39LNAVLASLL PIISWP 40 VLASLL PIISWP HTT 41 SLL PIISWP HTTKVQ 42 PIISWPHTTKVQGRT

Example 71 B7H3 Epitope Mapping

The experimental procedure outlined in the Example above, “TMEM211Epitope Mapping,” was followed to determine the epitope for an anti-B7H3antibody that can bind to B7H3 displayed on vesicles except as noted.The primary antibody was a rat anti-B7H3 monoclonal antibody. The B7H3antigen sequence was covered with 15-mer overlapping peptides having 12residue overlap, resulting in 174 peptides. Peptide sequences are shownin Table 68.

TABLE 68 B7H3 overlapping peptide library Peptide sequence SEQ ID NO.MLRRRGSPGMGVHVG 51 RRGSPGMGVHVGAAL 52 SPGMGVHVGAALGAL 53 MGVHVGAALGALWFS54 HVGAALGALWFSLTG 55 AALGALWFSLTGALE 56 GALWFSLTGALEVQV 57WFSLTGALEVQVPED 58 LTGALEVQVPEDPVV 59 ALEVQVPEDPVVALV 60 VQVPEDPVVALVGTD61 PEDPVVALVGTDATL 62 PVVALVGTDATLSSS 63 ALVGTDATLSSSFSP 64GTDATLSSSFSPEPG 65 ATLSSSFSPEPGFSL 66 SSSFSPEPGFSLAQL 67 FSPEPGFSLAQLNLI68 EPGFSLAQLNLIWQL 69 FSLAQLNLIWQLTDT 70 AQLNLIWQLTDTKQL 71NLIWQLTDTKQLVHS 72 WQLTDTKQLVHSFAE 73 TDTKQLVHSFAEGQD 74 KQLVHSFAEGQDQGS75 VHSFAEGQDQGSAYA 76 FAEGQDQGSAYANRT 77 GQDQGSAYANRTALF 78QGSAYANRTALFPDL 79 AYANRTALFPDLLAQ 80 NRTALFPDLLAQGNA 81 ALFPDLLAQGNASLR82 PDLLAQGNASLRLQR 83 LAQGNASLRLQRVRV 84 GNASLRLQRVRVADE 85SLRLQRVRVADEGSF 86 LQRVRVADEGSFTSF 87 VRVADEGSFTSFVSI 88 ADEGSFTSFVSIRDF89 GSFTSFVSIRDFGSA 90 TSFVSIRDFGSAAVS 91 VSIRDFGSAAVSLQV 92RDFGSAAVSLQVAAP 93 GSAAVSLQVAAPYSK 94 AVSLQVAAPYSKPSM 95 LQVAAPYSKPSMTLE96 AAPYSKPSMTLEPNK 97 YSKPSMTLEPNKDLR 98 PSMTLEPNKDLRPGD 99TLEPNKDLRPGDTVT 100 PNKDLRPGDTVTITS 101 DLRPGDTVTITSSSY 102PGDTVTITSSSYQGY 103 TVTITSSSYQGYPEA 104 ITSSSYQGYPEAEVF 105SSYQGYPEAEVFWQD 106 QGYPEAEVFWQDGQG 107 PEAEVFWQDGQGVPL 108EVFWQDGQGVPLTGN 109 WQDGQGVPLTGNVTT 110 GQGVPLTGNVTTSQM 111VPLTGNVTTSQMANE 112 TGNVTTSQMANEQGL 113 VTTSQMANEQGLFDV 114SQMANEQGLFDVHSI 115 ANEQGLFDVHSILRV 116 QGLFDVHSILRVVLG 117FDVHSILRVVLGANG 118 HSILRVVLGANGTYS 119 LRVVLGANGTYSSLV 120VLGANGTYSSLVRNP 121 ANGTYSSLVRNPVLQ 122 TYSSLVRNPVLQQDA 123SLVRNPVLQQDAHSS 124 RNPVLQQDAHSSVTI 125 VLQQDAHSSVTITPQ 126QDAHSSVTITPQRSP 127 HSSVTITPQRSPTGA 128 VTITPQRSPTGAVEV 129TPQRSPTGAVEVQVP 130 RSPTGAVEVQVPEDP 131 TGAVEVQVPEDPVVA 132VEVQVPEDPVVALVG 133 QVPEDPVVALVGTDA 134 EDPVVALVGTDATLR 135VVALVGTDATLRSSF 136 LVGTDATLRSSFSPE 137 TDATLRSSFSPEPGF 138TLRSSFSPEPGFSLA 139 SSFSPEPGFSLAQLN 140 SPEPGFSLAQLNLIW 141PGFSLAQLNLIWQLT 142 SLAQLNLIWQLTDTK 143 QLNLIWQLTDTKQLV 144LIWQLTDTKQLVHSF 145 QLTDTKQLVHSFTEG 146 DTKQLVHSFTEGRDQ 147QLVHSFTEGRDQGSA 148 HSFTEGRDQGSAYAN 149 TEGRDQGSAYANRTA 150RDQGSAYANRTALFP 151 GSAYANRTALFPDLL 152 YANRTALFPDLLAQG 153RTALFPDLLAQGNAS 154 LFPDLLAQGNASLRL 155 DLLAQGNASLRLQRV 156AQGNASLRLQRVRVA 157 NASLRLQRVRVADEG 158 LRLQRVRVADEGSFT 159QRVRVADEGSFTSFV 160 RVADEGSFTSFVSIR 161 DEGSFTSFVSIRDFG 162SFTSFVSIRDFGSAA 163 SFVSIRDFGSAAVSL 164 SIRDFGSAAVSLQVA 165DFGSAAVSLQVAAPY 166 SAAVSLQVAAPYSKP 167 VSLQVAAPYSKPSMT 168QVAAPYSKPSMTLEP 169 APYSKPSMTLEPNKD 170 SKPSMTLEPNKDLRP 171SMTLEPNKDLRPGDT 172 LEPNKDLRPGDTVTI 173 NKDLRPGDTVTITSS 174LRPGDTVTITSSSYR 175 GDTVTITSSSYRGYP 176 VTITSSSYRGYPEAE 177TSSSYRGYPEAEVFW 178 SYRGYPEAEVFWQDG 179 GYPEAEVFWQDGQGV 180EAEVFWQDGQGVPLT 181 VFWQDGQGVPLTGNV 182 QDGQGVPLTGNVTTS 183QGVPLTGNVTTSQMA 184 PLTGNVTTSQMANEQ 185 GNVTTSQMANEQGLF 186TTSQMANEQGLFDVH 187 QMANEQGLFDVHSVL 188 NEQGLFDVHSVLRVV 189GLFDVHSVLRVVLGA 190 DVHSVLRVVLGANGT 191 SVLRVVLGANGTYSS 192RVVLGANGTYSSLVR 193 LGANGTYSSLVRNPV 194 NGTYSSLVRNPVLQQ 195YSSLVRNPVLQQDAH 196 LVRNPVLQQDAHGSV 197 NPVLQQDAHGSVTIT 198LQQDAHGSVTITGQP 199 DAHGSVTITGQPMTF 200 GSVTITGQPMTFPPE 201TITGQPMTFPPEALW 202 GQPMTFPPEALWVTV 203 MTFPPEALWVTVGLS 204PPEALWVTVGLSVSL 205 ALWVTVGLSVSLIAL 206 VTVGLSVSLIALLVA 207GLSVSLIALLVALAF 208 VSLIALLVALAFVSW 209 IALLVALAFVSWRKI 210LVALAFVSWRKIKQS 211 LAFVSWRKIKQSSEE 212 VSWRKIKQSSEEENA 213RKIKQSSEEENAGAE 214 KQSSEEENAGAEDQD 215 SEEENAGAEDQDGEG 216ENAGAEDQDGEGEGS 217 GAEDQDGEGEGSKTA 218 DQDGEGEGSKTALQP 219GEGEGSKTALQPLKH 220 EGSKTALQPLKHSDS 221 KTALQPLKHSDSKED 222LQPLKHSDSKEDDGQ 223 LKHSDSKEDDGQEIA 224

FIGS. 121A-C show the results of the experiments. In the figures,positions 1-12 (leftmost row, from front to back) correspond to SEQ IDNOs: 51-62, positions 13-24 (second row from left, from front to back)correspond to SEQ ID NOs: 63-74, positions 25-36 (third row from left,from front to back) correspond to SEQ ID NOs: 75-86, positions 37-48(fourth row from left, from front to back) correspond to SEQ ID NOs:87-98, positions 49-60 (fifth row from left, from front to back)correspond to SEQ ID NOs: 99-110, positions 61-72 (sixth row from left,from front to back) correspond to SEQ ID NOs: 112-122, positions 73-84(seventh row from left, from front to back) correspond to SEQ ID NOs:123-134, positions 85-96 (eighth row from left, from front to back)correspond to SEQ ID NOs: 135-146, positions 97-108 (ninth row fromleft, from front to back) correspond to SEQ ID NOs: 147-158, positions109-120 (tenth row from left, from front to back) correspond to SEQ IDNOs: 159-170, positions 121-132 (eleventh row from left, from front toback) correspond to SEQ ID NOs: 171-182, positions 133-144 (twelfth rowfrom left, from front to back) correspond to SEQ ID NOs: 183-194,positions 145-156 (thirteenth row from left, from front to back)correspond to SEQ ID NOs: 195-206, positions 157-168 (fourteenth rowfrom left, from front to back) correspond to SEQ ID NOs: 207-218, andpositions 169-174 (fifteenth row from left, from front to back)correspond to SEQ ID NOs: 219-224. See Table 68 for sequences. Positions175 and 176 are the positive controls and positions 177 and 178 arenegative controls.

FIG. 121A shows results of incubation of the plate with the anti-B7H3rat monoclonal antibody and goat anti-rat HRP secondary antibody. In thefigure, peptides at positions 10, 11, 83 and 84 gave a clear signal.These positions correspond to SEQ ID NOs: 60, 61, 133 and 134,respectively. FIG. 121B shows results of incubation of the plate with acontrol rat polyclonal antibody and goat anti-rat HRP secondaryantibody. The positive controls gave a strong signal. FIG. 121C showsresults of incubation of the plate with the anti-B7H3 rat monoclonalantibody and goat anti-rabbit IgG HRP secondary antibody. Because thesecondary antibody recognizes rabbit IgG, this experiment also serves asa control. Only the positive controls gave a signal.

According to these experiments, a series of overlapping peptides wereidentified that are recognized by the anti-B7H3 antibody, correspondingto SEQ ID NOs: 60, 61, 133 and 134. These peptides are equivalent to thesequence “ALEVQVPEDPVVALVGTDA” (SEQ ID NO: 225). As shown in Table 69,the common epitope contained in the four overlapping peptides isVQVPEDPVVALVG (SEQ ID NO: 226). This indicates that vesicles can beidentified using an antibody that recognizes all or part of the epitopeVQVPEDPVVALVG (SEQ ID NO: 226).

TABLE 69 Anti-B7H3 common epitope SEQ ID NO. Peptide sequence 60 ALEVQVPEDPVVALV 61 VQVPEDPVVALVG TD 133 VE VQVPEDPVVALVG 134 QVPEDPVVALVGTDA

Example 72 CD9 Epitope Mapping

This study was performed to identify the epitope of a mouse anti-CD9monoclonal antibody that can be used to detect CD9 displayed on avesicle surface. Epitope mapping was performed by panning a phagedisplay peptide library. Several peptides were identified thatspecifically bound to that anti-CD9 antibody but did not bind to acontrol mouse IgG2b antibody.

Materials used include the following: Ph.D.-12 Phage Display LibraryKit, Cat. No. E8100S, New England Biolabs (Ipswich, Mass.); HorseradishPeroxidase (HRP)/Anti-M13 Monoclonal Conjugate, Cat. No. 27-9421-01, GEHealthcare (Waukesha, Wis.); Amine-binding, Maleic Anhydride ActivatedClear Strip MicroPlates, Cat. No. 15100, Pierce, Part of Thermo FisherScientific (Rockford, Ill.); Sequencing primer M13 (−96) 5′-CCC TCA TAGTTA GCG TAA CG-3′ (SEQ ID NO. 227). The target antibody was a mouseanti-CD9 monoclonal antibody and the control antibody was a mouse IgG2bantibody. The target and control antibodies were dialyzed in phosphatebuffered saline (PBS; pH 7.6) to increase the coating efficiency on theamine-binding plates. Three rounds of library panning were performed.Experiments were performed using methods described in the Ph.D.™ PhageDisplay Libraries Instruction Manual, Version 1.0, New England BioLabs;and Stone et al. Mol Immunol, 2007, 44:2487-91. Further details areprovided below.

In the first round of panning, the microplate well was coated withdialyzed target antibody (200 μg/ml) in PBS (pH 7.6) at 4° C.,overnight. The plates were washed five times with PBS-T (0.05% Tween 20in PBS) and blocked with M-TBS (2% skimmed milk in tris-buffered saline(TBS)) at 37° C. for 2 hours. After five washes with PBS-T, the phagelibrary (10¹¹ pfu) was added to the blocked wells and incubated on ashaker at room temperature (RT) for 1 hour. Unbound phages werediscarded and the plate was washed six times with PBS-T and anotherthree times with PBS. Bound phage particles were eluted into a M-PBSblocked 1.5 ml centrifuge tube by incubating with 200 μl 0.1 MTriethylamine (TEA) for 6 minutes. The particles were then neutralizedwith 100 μl of 1 M Tris-HCl (pH 7.4) for 10 minutes. The eluted phageswere titered and amplified for 5 hours for the next round panning.

A subtractive panning strategy was used in the second and third roundsof panning. The blocking buffers were alternated for each round ofpanning. First, the microplate wells were coated with control and targetantibody respectively, and blocked with 3% BSA in TBS. The amplifiedphage from the first round of panning (10¹¹ pfu) were incubated incontrol coated wells in advance to subtract phage which bound to themouse IgG2b. The phage remaining after subtraction were incubated intarget coated wells at RT for 1 hour. Non-bound phage were discarded andthe bound phage were eluted, titered and amplified for the third roundof panning. The panning procedure of the third round of panning wassimilar with the second round, the only difference was that the blockedbuffer was alternated with 2% M-TBS.

After three rounds of panning, 92 single plaques were picked for phageenzyme-linked immunosorbent assay (ELISA) test and promising clones weresequenced. One ml of phage stock was incubated overnight in a culture ofE. coli K12 strain ER2738 in 100 ml LB medium; 1 ml of diluted culturewas dispensed into each well of a 96 deep-well plate. Ninety-two wellswere incubated with the plaques from the third round titer plates, andtwo positive control (E12, F12) and negative controls (G12, H12) wereincubated in the remaining four wells. The plate was incubated at 37° C.with shaking at 250 rpm for 4.5-5 hours. The plate was then spun at4,000 rpm for 30 minutes at 4° C. The supernatant was collected forphage ELISA against the target and control antibody.

The ELISA was performed by coating two 96-well microplates with either 1μg/ml target or control antibody overnight at 4° C., washing the platesthree times with 0.05% PBS-T, blocking with 2% M-PBS at 37° C. for 2hours, repeating the washing step, adding 100 μl collected supernatantto each well, then incubating the plates overnight at 4° C. Washing wasrepeated and any remaining phage were detected by incubating with 1:5000diluted HRP/anti-M13 antibody at 37° C. for 1 hour. The plates weredeveloped by colorimetric assay and absorbance was measured at 450 nm.Positive clones were identified according to the absorbance. The singlestranded DNA (ssDNA) of positive phages was extracted for sequencinganalysis.

FIGS. 122A-B show the results of ELISA screening the output phage fromthree rounds of panning with the target antibody (FIG. 122A) or ananti-mouse IgG control antibody (FIG. 122B). In the figures, positions1A-12D correspond to positive clones identified above. Positions E12 andF12 are positive controls comprising the amplified library of the outputphage from the second and third rounds of panning. Positions H12 and G12are negative controls comprising the supernatant of ER2738 culture.

Twenty positive clones were chosen for sequencing to reveal the peptidesequences displayed by phage that were recognized by the anti-CD9 targetantibody. The sequencing results identified several peptides, as shownin Table 70. In Table 70, the clone number corresponds to the positionin the well as shown in FIG. 88A.

TABLE 70 Peptides identified by the Phagelibrary screening with anti-CD9 Peptide Number SEQ Positive Clone Numbersequence of Clones ID NO. E1, F1, D3, D5, F5, RINMNYMINHMM 7 228 C7, C9C1, B5, F7, F9, D12 AIGWNYPTEMIR 5 229 H5, A12 HTAKQMMSYMIR 2 230 H3WFYDSQMIMDSA 1 231 A5 SMIHRLQAHNIM 1 232 C5 SMWHTLGRHWIA 1 233 E5THRDASWIRADL 1 234 E9 VPLHHSQMYPPK 1 235 H9 WLHPAQPVNHMF 1 236

In conclusion, after three rounds of library panning and phage ELISAassays, several positive clones were found that bound to targetantibody. Twenty positive clones were chosen for ssDNA extraction andsequencing. Nine peptide sequences were identified from the sequencingresults. As shown in Table 70, seven clones displayed the sequenceRINMNYMINHMM (SEQ ID NO. 228), another five displayed the sequenceAIGWNYPTEMIR (SEQ ID NO. 229) and another two displayed the sequenceHTAKQMMSYMIR (SEQ ID NO. 230). The remaining six clones (SEQ ID NOs.231-236) were unique. The peptides can be used as epitope mimics for thequantitative measure of the amount of anti-CD9 antibody.

Although preferred embodiments of the present invention have been shownand described herein, it will be obvious to those skilled in the artthat such embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the invention. It should be understoodthat various alternatives to the embodiments of the invention describedherein may be employed in practicing the invention. It is intended thatthe following claims define the scope of the invention and that methodsand structures within the scope of these claims and their equivalents becovered thereby.

1. A method of characterizing a prostate cancer comprising: a) isolatingmicroRNA from a biological sample; b) determining a presence or a levelof one or more microRNA selected from the group consisting ofhsa-miR-100, hsa-miR-1236, hsa-miR-1296, hsa-miR-141, hsa-miR-146b-5p,hsa-miR-17*, hsa-miR-181a, hsa-miR-200b, hsa-miR-20a*, hsa-miR-23a*,hsa-miR-331-3p, hsa-miR-375, hsa-miR-452, hsa-miR-572, hsa-miR-574-3p,hsa-miR-577, hsa-miR-582-3p, hsa-miR-937, miR-10a, miR-134, miR-141,miR-200b, miR-30a, miR-32, miR-375, miR-495, miR-564, miR-570,miR-574-3p, and miR-885-3p; and c) comparing the presence or the levelof the group of microRNA to a reference, thereby characterizing theprostate cancer.
 2. (canceled)
 3. (canceled)
 4. (canceled)
 5. The methodof claim 1, wherein the characterizing comprises identifying theprostate cancer as metastatic or aggressive.
 6. The method of claim 5,wherein the one or more microRNA comprises hsa-miR-100, hsa-miR-1236,hsa-miR-1296, hsa-miR-141, hsa-miR-146b-5p, hsa-miR-17*, hsa-miR-181a,hsa-miR-200b, hsa-miR-20a*, hsa-miR-23a*, hsa-miR-331-3p, hsa-miR-375,hsa-miR-452, hsa-miR-572, hsa-miR-574-3p, hsa-miR-577, hsa-miR-582-3p,hsa-miR-937, miR-10a, miR-134, miR-141, miR-200b, miR-30a, miR-32,miR-375, miR-495, miR-564, miR-570, miR-574-3p, and miR-885-3p.
 7. Themethod of claim 1, wherein the one or more microRNA comprises miR-495,miR-10a, miR-30a, miR-570, miR-32, miR-885-3p, miR-564, and miR-134. 8.The method of claim 1, wherein the one or more microRNA compriseshsa-miR-200b, hsa-miR-375, hsa-miR-582-3p, hsa-miR-17*, hsa-miR-1296,hsa-miR-20a*, hsa-miR-100, hsa-miR-452, and hsa-miR-577; hsa-miR-375,hsa-miR-452, hsa-miR-200b, hsa-miR-146b-5p, hsa-miR-1296, hsa-miR-17*,hsa-miR-100, hsa-miR-574-3p, hsa-miR-20a*, hsa-miR-572, hsa-miR-1236,hsa-miR-181a, hsa-miR-937, and hsa-miR-23a*.
 9. The method of claim 1,wherein the one or more microRNA comprises hsa-miR-375, hsa-miR-452,hsa-miR-200b, hsa-miR-146b-5p, hsa-miR-1296, hsa-miR-17*, hsa-miR-100,hsa-miR-574-3p, hsa-miR-20a*, hsa-miR-572, hsa-miR-1236, hsa-miR-181a,hsa-miR-937, and hsa-miR-23a*.
 10. The method of claim 1, wherein theone or more microRNA comprises miR-141, miR-375, miR-200b andmiR-574-3p.
 11. The method of claim 1, wherein the one or more microRNAcomprises hsa-miR-200b, hsa-miR-375, hsa-miR-141, hsa-miR-331-3p,hsa-miR-181a, hsa-miR-574-3p.
 12. The method of claim 1, wherein thestep of comparing the presence or the level of the group of microRNA tothe reference comprises determining whether any of the one or moremicroRNA is altered relative to the reference, and thereby providing aprognostic, diagnostic or theranostic determination for the prostatecancer.
 13. (canceled)
 14. (canceled)
 15. (canceled)
 16. (canceled) 17.(canceled)
 18. The method of claim 1, wherein the biological samplecomprises a bodily fluid.
 19. The method of claim 18, wherein the bodilyfluid comprises peripheral blood, sera, plasma, ascites, urine,cerebrospinal fluid (CSF), sputum, saliva, bone marrow, synovial fluid,aqueous humor, cerumen, broncheoalveolar lavage fluid, semen, prostaticfluid, cowper's fluid or pre-ejaculatory fluid, sweat, fecal matter,tears, cyst fluid, pleural fluid, peritoneal fluid, pericardial fluid,lymph, chyme, chyle, bile, interstitial fluid, pus, sebum, vomit,mucosal secretion, stool water, pancreatic juice, lavage fluids fromsinus cavities, bronchopulmonary aspirates, blastocyl cavity fluid, or acombination thereof.
 20. The method of claim 1, wherein the biologicalsample comprises urine, blood or blood derivatives.
 21. The method ofclaim 1, wherein the biological sample comprises one or moremicrovesicle.
 22. The method of claim 21, wherein the one or moremicroRNA comprises payload within the one or more microvesicle.
 23. Themethod of claim 21, wherein the one or more microvesicle has a diameterbetween 20 nm and 800 nm.
 24. The method of claim 21, wherein the one ormore microvesicle has a diameter between 20 nm and 200 nm.
 25. Themethod of claim 21, wherein the one or more microvesicle is subjected tosize exclusion chromatography, density gradient centrifugation,differential centrifugation, nanomembrane ultrafiltration,immunoabsorbent capture, affinity purification, affinity capture,immunoassay, microfluidic separation, flow cytometry or combinationsthereof.
 26. The method of claim 21, wherein the one or moremicrovesicle is contacted with one or more binding agent.
 27. The methodof claim 26, wherein the one or more binding agent comprises a nucleicacid, DNA molecule, RNA molecule, antibody, antibody fragment, aptamer,peptoid, zDNA, peptide nucleic acid (PNA), locked nucleic acid (LNA),lectin, peptide, dendrimer, membrane protein labeling agent, chemicalcompound, or a combination thereof.
 28. The method of claim 26, whereinthe one or more binding agent is used to capture and/or detect the oneor more microvesicle.
 29. The method of claim 26, wherein the one ormore binding agent binds to one or more surface antigen on the one ormore microvesicle.
 30. (canceled)
 31. The method of claim 29, whereinthe one or more surface antigen comprises one or more of CD9, CD63,CD81, PSMA, PCSA, B7H3 and EpCam.
 32. The method of claim 29, whereinthe one or more surface antigen comprises one or more of a tetraspanin,CD9, CD63, CD81, CD63, CD9, CD81, CD82, CD37, CD53, Rab-5b, Annexin V,MFG-E8, or a protein in Table
 3. 33. The method of claim 26, wherein theone or more binding agent is used to isolate or capture the one or moremicrovesicle.
 34. The method of claim 33, wherein the one or moremicroRNA comprises payload within the one or more isolated or capturedmicrovesicle. 35-74. (canceled)
 75. The method of claim 29, wherein theone or more surface antigen comprises one or more of CD9, PSMA, PCSA,CD63, CD81, B7H3, IL6, OPG-13, IL6R, PA2G4, EZH2, RUNX2, SERPINB3, andEpCam.
 76. The method of claim 29, wherein the one or more surfaceantigen comprises one or more of A33, a33 n15, AFP, ALA, ALIX, ALP,AnnexinV, APC, ASCA, ASPH (246-260), ASPH (666-680), ASPH (A-10), ASPH(D01P), ASPH (D03), ASPH (G-20), ASPH(H-300), AURKA, AURKB, B7H3, B7H4,BCA-225, BCNP1, BDNF, BRCA, CA125 (MUC16), CA-19-9, C-Bir, CD1.1, CD10,CD174 (Lewis y), CD24, CD44, CD46, CD59 (MEM-43), CD63, CD66e CEA, CD73,CD81, CD9, CDA, CDAC1 1a2, CEA, C-Erb2, C-erbB2, CRMP-2, CRP, CXCL12,CYFRA21-1, DLL4, DR3, EGFR, Epcam, EphA2, EphA2 (H-77), ER, ErbB4, EZH2,FASL, FRT, FRT c.f23, GDF15, GPCR, GPR30, Gro-alpha, HAP, HBD 1, HBD2,HER 3 (ErbB3), HSP, HSP70, hVEGFR2, iC3b, IL6 Unc, IL-1B, IL6 Unc, IL6R,IL8, IL-8, INSIG-2, KLK2, L1CAM, LAMN, LDH, MACC-1, MAPK4, MART-1,MCP-1, M-CSF, MFG-E8, MIC1, MIF, MIS RII, MMG, MMP26, MMP7, MMP9, MS4A1,MUC1, MUC1 seq1, MUC1 seq11A, MUC17, MUC2, Ncam, NGAL, NPGP/NPFF2, OPG,OPN, p53, p53, PA2G4, PBP, PCSA, PDGFRB, PGP9.5, PIM1, PR (B), PRL, PSA,PSMA, PSME3, PTEN, R5-CD9 Tube 1, Reg IV, RUNX2, SCRN1, seprase,SERPINB3, SPARC, SPB, SPDEF, SRVN, STAT 3, STEAP1, TF (FL-295), TFF3,TGM2, TIMP-1, TIMP1, TIMP2, TMEM211, TMPRSS2, TNF-alpha, Trail-R2,Trail-R4, TrKB, TROP2, Tsg 101, TWEAK, UNC93A, VEGF A, and YPSMA-1. 77.The method of claim 29, wherein the one or more surface antigencomprises one or more of 5T4, ACTG1, ADAM10, ADAM15, ALDOA, ANXA2,ANXA6, APOA1, ATP1A1, BASP1, C1orf58, C20orf114, C8B, CAPZA1, CAV1,CD151, CD2AP, CD59, CD9, CD9, CFL1, CFP, CHMP4B, CLTC, COTL1, CTNND1,CTSB, CTSZ, CYCS, DPP4, EEF1A1, EHD1, ENO1, F11R, F2, F5, FAM125A,FNBP1L, FOLH1, GAPDH, GLB1, GPX3, HIST1H1C, HIST1H2AB, HSP90AB1, HSPA1B,HSPA8, IGSF8, ITGB1, ITIH3, JUP, LDHA, LDHB, LUM, LYZ, MFGE8, MGAM,MMP9, MYH2, MYL6B, NME1, NME2, PABPC1, PABPC4, PACSIN2, PCBP2, PDCD6IP,PRDX2, PSA, PSMA, PSMA1, PSMA2, PSMA4, PSMA6, PSMA7, PSMB1, PSMB2,PSMB3, PSMB4, PSMB5, PSMB6, PSMB8, PTGFRN, RPS27A, SDCBP, SERINC5,SH3GL1, SLC3A2, SMPDL3B, SNX9, TACSTD1, TCN2, THBS1, TPI1, TSG101, TUBB,VDAC2, VPS37B, YWHAG, YWHAQ, and YWHAZ.